The disclosure describes example devices and methods for amplifying nucleic acids by using an oil layer including exothermal particles, forming an emulsion using the oil layer including exothermal particles, and performing a nucleic acids amplification reaction in the emulsion. The nucleic acids amplification reaction may be effectively performed even with a small amount of a sample by maximizing nucleic acids amplification efficiency, reducing reaction time, and improving controls to be convenient.

A nucleic acid amplification reaction is a technique for sequentially amplifying a region of nucleic acids (including specific nucleotide sequences, a so-called target site) by repeatedly heating and cooling a sample (including the nucleic acids) to exponentially amplify the nucleic acids including the target site. Nucleic acid amplification reactions are widely used in areas such as biotechnology, genetic engineering, and medical fields. For example, polymerase chain reaction (PCR) is a technique for exponentially amplifying deoxyribonucleic acids (DNAs) including the specific nucleotide sequences by repeatedly (1) denaturing a sample including double-stranded DNAs by heating the sample to a temperature of about 95° C. to isolate the double-stranded DNAs to single-stranded DNAs; (2) providing forward and reverse oligonucleotide primers, which include sequences complementary to the specific nucleotide sequences about to be amplified, to the sample after denaturing; (3) annealing to form a partial DNA-primer complex binding the primers to the specific nucleotide sequences of the single-stranded DNAs by cooling the primers and the denatured single-stranded DNAs to a temperature of about 55° C. and (4) extension synthesizing double stranded DNAs based on primers of the partial DNA-primer complex by the DNA polymerase (e.g., Tag polymerase) while maintaining a temperature of the sample at about 72° C. after the annealing.
Since a nucleic acid amplification reaction is a procedure preceded or premised in most fields of its application, the nucleic acid amplification reaction needs to be proceeded accurately and rapidly. Therefore, various nucleic acid amplification techniques, which may derive reliable nucleic acid amplification results with a small amount of sample and may reduce reaction time, have recently been introduced. Moreover, nucleic acid amplification devices for easily performing the nucleic acid amplification reaction by controlling various samples and reagents in a single instrument are currently being developed.

Device and method of amplifying nucleic acids by using oil layer comprising exothermal particles


A connector with two magnets with like poles facing towards each other. A first magnet of the two magnets is fixedly attached to a support base and female receptacle situated above the same pole of the first magnet. The second magnet is fixedly attached to a male flange adapted for passage through the female receptacle. The male flange and second magnet are further fixedly attached to a length of material which is longer than the female receptacle, such that the male flange remains within the female receptacle in an assembled condition due to a magnetic force pushing each magnet away from the other. Another embodiment includes a method of use, including lining up the male flange into the female receptacle for locking and pushing down and slightly transversely on the male flange to unlock.

Clasps and buckles are well known in the art. These articles generally function by affixing or joining two ends together by way of a fastener. The two ends may be two ends of the same device, such as a length of fabric or rope, or may be two ends of two different devices. With a magnetic clasp or fastener, such as, for example, with a doorstop or computer security device, magnets or electromagnets of opposite polarity are attracted to each other. The above are the most commonly known clasps and buckles in the art.

This patent is subject to a terminal disclaimer.


Technology is disclosed for transferring money anonymously between a sender and a recipient by use of a token. In some embodiments, the disclosed technology involves communication between a mobile payment application (“App”) and a payment service system (PSS). Upon the recipient's request, the App, installed on the recipient's device, generates the token, and provides the token to both the recipient and the PSS. The PSS receives both the token and the recipient's identification information from the App, associates the token with the identification information, and stores such association. The recipient, on the other hand, transfers the token to the sender, who forwards it to the PSS to request a money transfer. The PSS receives the token and identifies the recipient's identification information based on the previously stored association with the token. With the identification information, the PSS determines the recipient's financial account to execute the money transfer.

As society becomes increasingly "cashless" through the increased use of electronic wallets and other digital forms of money transfer, peer-to-peer money transfers ("P2P transfers") are becoming increasingly popular between individuals, who may or may not personally know each other. An example is the payment of an "IOU" debt from an individual (payer) to an acquaintance (payee) at a social group outing, where the individual and the distant acquaintance may never socialize again. Another example is the buying and selling of goods at an arts-and-crafts fair, where a buyer (payer) makes a payment to a seller (payee) whom the buyer may never see again.
Decreased reliance on cash often means that individuals have insufficient cash on hand, or even paper checks. Further, for a payee who participates in frequent P2P transfers (e.g., arts-and-crafts fair seller), managing and tracking money transfers from multiple payers can be cumbersome and highly inconvenient. The more convenient payment methods, such as credit cards or bank account debits through electronic funds transfers (EFT), are often available only to retail businesses, as opposed to individual payers and payees. However, even if such payment methods were available, completion of the payments often requires an account setup between the individual payers and payees, necessitating the individuals to reveal information about their personal identities.


A control circuit having a drive circuit and a controller adapted for use in optical mice is disclosed. The drive circuit is connected between first and second ports and passes a current between those ports having an amplitude determined by a control signal that is generated by the controller. The first port is connected to an illumination device and the second port is connected to a power rail. The control circuit also includes a potential measuring circuit that generates a signal indicative of a drive circuit potential between the first and second ports. The controller records the drive circuit potential for a predetermined current when the first port is connected to an illumination device. The controller sets the control signal based on the recorded drive circuit potential. The control circuit can record the drive circuit potential when the controller is powered.

Pointing devices for use in computers, and the like, are well known. These devices are often referred to as mice. In general, a mouse is a handheld device that is moved by the user over a surface to control the movement of a cursor on a display screen that is connected to the mouse. Typically, the cursor is moved in a direction and by an amount determined by the amount that the mouse moved over the surface. The original mice sensed the motion of a handheld device over a surface using mechanical means such as a ball that rolled against a set of rollers as the mouse was moved over the surface. However, this type of mechanical system suffered from a number of problems such as the accumulation of grease and other debris on the rollers as a result of material picked up from the surface by the ball.
These problems have been overcome by mice based on optical sensing. An optical mouse typically includes a light source that illuminates the surface under the device at an oblique angle and a camera that takes a series of pictures of the illuminated surface. The light source typically includes a light-emitting diode (LED) and a lens for directing the light to the appropriate area of the surface at an angle that enhances any surface roughness. By comparing successive pictures, the mouse controller can determine the distance and direction that the mouse moved between the two pictures. This comparison typically involves shifting one of the images relative to the other and then computing some measurement of the correlation of the two images. The shift that provides the maximum correlation is then assumed to be the amount of displacement of the mouse between the two pictures.
These devices work well on surfaces that have a texture that gives rise to images that can easily be compared to one another by shifting one of the images. However, many surfaces are less than ideal. For example, a conventional optical mouse moving over a glass surface performs poorly, if at all, either because the surface is too smooth to provide the desired image or because the surface is transparent, and the underlying surface is out of focus. Other surfaces provide tracking information that has insufficient precision for many applications.
To provide optical mice that perform better on these less than ideal surfaces, optical mice having a laser diode in place of the LED have been developed. When the surface is illuminated with coherent light from the laser, the resulting images are enhanced due to the interference of the coherent light reflected from the surface. Optical mice based on a number of such interference effects have been suggested.
While these coherent light mice provide improvement over the LED-based mice, they do so at an increased cost. Hence, manufacturers of mice tend to offer a range of products having different levels of performance and different costs. The mice can differ in the light source, the image sensor, or the complexity of the control chip that controls the image sensor and computes the movement of the mouse over the surface. As a result, the manufacturer must stock parts for many mouse designs. The introduction of laser-based mice has further increased the number of parts that must be maintained in inventory. In general, the mouse includes a controller chip that sets the current that flows through the light source and performs the various calculations needed to compute a displacement. LED light sources require different driver circuits than laser diode light sources. Hence, the manufacturer must stock both new controller chips and lasers in addition to the parts utilized in the assembly of LED-based mice. In addition, the design time associated with developing a new optical mouse having a different light source from those currently used is increased by the need to provide a new control chip for the new light source.
In principle, the driver circuitry could be located on a separate chip so that the same controller chip could be used for both mice based on LEDs and lasers. However, this solution increases the cost of both types of mice. Such cost issues are particularly important in low-cost mice.

Driving circuit adapted for use in optical mice

Avago Technologies General IP (Singapore) Pte. Ltd.


An ultra-thin semiconductor package device comprises a heat-resistant film-type adhesive support tape which connects a semiconductor chip to a plurality of individual lead frames, wherein each lead frame is connected to an associated one of a plurality of electrode pads of the semiconductor chip by a plurality of bonding wires. An encapsulating molding material provides environmental protection for the completed package. Within the encapsulating molding, the semiconductor chip is mounted on a same underside of the support tape as the plurality of lead frames, such that the bottom of the semiconductor chip is aligned with the bottom of an encapsulating molding, and the height of a loop in each bonding wire is minimized.

1. Field of the Invention
The present invention relates to semiconductor packaging technology.
More particularly, the present invention relates to an ultra-thin semiconductor package having a thickness of 0.5 mm using a film-type adhesive support tape.
2. Description of Related Art
Recently, as the supply of various portable electronic devices such as digital cameras, MP3 players, handheld personal computers (HPC) and personal digital assistants (PDA) expands, new technologies are required to produce semiconductor packages having a smaller and thinner structure.
FIG. 1 illustrates an end view of a conventional semiconductor package device 10 using a lead frame. The package device 10 includes a semiconductor chip 1 and a lead frame 2. The lead frame 2 includes a plurality of leads 4 and a die pad 3 to which the semiconductor chip 1 is attached using an adhesive 5. Each one of the plurality of leads 4 is connected to semiconductor chip 1 by a bonding wire 6 and further includes an inner lead 4a encapsulated by a molding body 7 and an outer lead 4b exposed from the molding body 7 and having a structure suitable for mounting on an external device.
In conventional package 10, total thickness of the package 10 is determined by the thickness of the semiconductor chip 1, the die pad 3, the adhesive 5 and a loop in each bonding wire 6. The thickness of a semiconductor chip 1 manufactured from an 8-inch wafer, the adhesive 5, the wire loop and the die pad 3 are generally 300 xcexcm, 50 xcexcm, 80-100 xcexcm and 100-150 xcexcm, respectively. Due to the thickness of the molding body 7 above and below the semiconductor chip 1 and a stand-off of the outer lead 4b, it is impossible to embody a conventional package having a thickness of less than 0.5 mm.
FIG. 2 illustrates an end view of a conventional semiconductor package device having a lead-on-chip (LOC) structure. In a package 20 shown in FIG. 2, a plurality of electrode pads are formed in the center of an active surface of a semiconductor chip 11, and an inner lead 14a of each one of a plurality of leads 14 is directly attached to the periphery of the active surface. In this structure, each lead 14 is mounted on the semiconductor chip 11. Each inner lead 14a is attached to a chip surface by an adhesive 13 and is electrically connected to an electrode pad of the semiconductor chip 11 by a bonding wire 16. The semiconductor chip 11, inner leads 14a and bonding wires 16 are encapsulated by a molding body 17. An outer lead 14b of each of the plurality of leads 14 is exposed from the molding body 17.
Disadvantageously, although the inner lead 14a in the package 20 of FIG. 2 functions similar to the die pad 3 in the package 10 of FIG. 1, total thickness of a package cannot be reduced due to the wire loop 16 above the inner lead 14a. Further, although conventional semiconductor packages may be made thinner by reducing the thickness of the semiconductor chip and/or the lead frame, such thinner elements are less durable and may be easily broken during routine handling.
According to a feature of an embodiment of the present invention, there is provided an ultra-thin semiconductor package device using plastic package technology.
According to another feature of an embodiment of the present invention, there is provided a semiconductor package device having a thickness of less than 0.5 mm capable of maintaining proper thickness of a semiconductor chip or a lead frame.
According to an aspect of an embodiment of the present invention, a semiconductor package device uses a heat-resistant support tape instead of a die pad as a lead frame. The support tape is preferably a film-type adhesive tape. The semiconductor package device includes a semiconductor chip having an active surface in which a plurality of electrode pads are formed and a bottom surface opposite of the active surface, a plurality of lead frames connected to each electrode pad of the semiconductor chip, including an inner lead having an upper surface, wherein the height of the upper surface is equal to the height of the active surface (i.e., the two surfaces are aligned), a plurality of bonding wires, each electrically connecting one electrode pad of the semiconductor chip with an associated inner lead, and a molding body encapsulating the semiconductor chip, the support tape and the plurality of bonding wires, wherein the heat-resistant support tape is attached to the upper surface of the inner lead and the active surface of the semiconductor chip and supports the inner lead and the semiconductor chip.
The support tape includes a plurality of electrode pad opening areas and a plurality of bonding part opening areas for exposing the electrode pads of the semiconductor chip and a bonding part of the inner lead to which each of the plurality of the bonding wires are attached. Each electrode pad opening area of the support tape is individually formed to align with each of electrode pads or is formed as one body to align with a row of electrode pads. Also, the support tape may include an active surface opening area partially exposing a portion of the active surface of the semiconductor chip, which is directly contacted to the molding body. Preferably, a bottom surface of the molding body and the bottom surface of the semiconductor chip are aligned, and the bottom surface of the semiconductor chip is exposed from the molding body.
According to another aspect of an embodiment of the present invention, a semiconductor package device is provided which includes a semiconductor chip having an active surface in which a plurality of electrode pads are formed and a bottom surface opposite of the active surface, a plurality of lead frames, each connected to one of the plurality of electrode pads of the semiconductor chip, and each one including a plurality of inner leads having an upper surface, wherein the height of the upper surface is equal to the height of the active surface (i.e., the two surfaces are aligned), a heat-resistant support tape having a plurality of first bonding parts, a plurality of second bonding parts and a routing pattern connecting each first bonding part with an associated second bonding part, attached to the upper surface of the inner lead and the active surface of the semiconductor chip, and supporting the plurality of inner leads and the semiconductor chip. A first bonding wire electrically connects each one of the electrode pads of the semiconductor chip with an associated one of the first bonding parts, and a second bonding wire electrically connects each one of the second bonding parts with an associated one of the inner leads. After the wire bonding is completed, a molding body encapsulates the semiconductor chip, the support tape and the first and second bonding wires.
The support tape further includes an electrode pad opening area and a bonding part opening area for exposing the plurality of electrode pads of the semiconductor chip and a third bonding part of the inner lead to which each first and second bonding wires are attached. Also, the support tape may further include an active surface opening area partially exposing a portion of the active surface of the semiconductor chip, and the exposed portion is directly contacted to and adhering to the molding body. The support tape may be wholly or partially attached to the active surface of the semiconductor chip.
These and other features and aspects of the present invention will be readily apparent to those of ordinary skill in the art upon review of the detailed description that follows.

Ultra-thin semiconductor package device using a support tape


The invention relates to a method for regulating a gas engine (1) having a generator (5), wherein a regulator torque is calculated by means of a speed regulator from a speed regulator deviation, wherein a target volume flow is calculated at least as a function of the regulator torque, wherein a fuel volume is determined as a proportion of a fuel-air mixture as a function of the target volume flow, and wherein a target receiver pipe pressure is also calculated as a function of the target volume flow as a guide parameter for a receiver pipe pressure regulating circuit for regulating the mixture pressure (pRRA, pRRB) of a fuel-air mixture in the receiver pipe (12, 13) above the inlet valves of the gas engine (1). The invention is characterized in that a deviation of the regulator torque from a generator torque is calculated and the target receiver pipe pressure is corrected using the deviation.

The invention concerns a method for automatically controlling a gas engine, in which both a fuel volume as a fraction of an air/fuel mixture is determined as a function of a set volume flow and a mixture pressure of the air/fuel mixture in the intake manifold upstream of the intake valves of the gas engine is determined as a function of a set volume flow.
Gas engines are often used as power plants for emergency generators, standby-ready units, or combined heat and power (CHP) installations. In these applications, the gas engine is operated at a combustion air ratio of, for example, 1.7, i.e., in a lean operation with air excess. Typically, the gas engine has a gas throttle valve for setting the gas fraction in the air/fuel mixture, a mixer for mixing the combustible gas with the air, a compressor as part of an exhaust gas turbocharger, a cooler and a mixture throttle valve. The mixture throttle valve serves to set the filling of the working cylinders and thus the torque of the gas engine. The filling of the working cylinders in turn is computed from the pressure of the air/fuel mixture with other parameters otherwise held constant, for example, at constant intake manifold temperature, at constant engine speed, and at constant combustion air ratio.
DE 10 2007 045 195 B3 discloses an automatic control method for a stationary gas engine with a generator, in which a speed controller uses a speed control deviation to determine a controller torque as a correcting variable. The controller torque in turn and the actual speed are used to determine a set volume flow by means of an efficiency input-output map. The set volume flow is both the input variable for controlling the gas throttle valve and the input variable for setting the mixture pressure in the intake manifold. The central element is the parallel control of the two control elements as a function of the same actuating variable, in this case, the set volume flow. The mixture pressure in the intake manifold is set via a cascade closed-loop pressure control system for the intake manifold. In this intake manifold closed-loop control system, the set intake manifold pressure represents the reference input and the measured intake manifold pressure is the controlled variable. The gas motor and the generator then constitute the controlled system. The set intake manifold pressure is computed from the set volume flow, taking into account the actual speed of the gas engine, the temperature in the intake manifold, and constants. Constant values include, for example, the combustion air ratio and a stoichiometric air requirement. The method we have been describing has been found to be effective in actual practice. However, the effect of different gas grades (volume fraction) within the same family of gases on the emission values remains critical.
DE 699 26 036 T2 also describes a method for automatically controlling a gas engine, in which a control signal for controlling the mixture throttle valve is computed from the speed control deviation by a PID controller. A correction value is determined, likewise as a function of the speed control deviation, and is then used to change the control signal for the gas throttle valve. However, the objective of the method is to suppress engine speed oscillations that develop after a change in the set engine speed.


An apparatus for determining the concentration of a gas in a vacuum chamber is described. Internal to the vacuum chamber are one or more cathodes. The chamber is fed via a gas inlet and is connected to a separate measurement chamber for accommodating a measuring device via a connecting line between the two chambers. The connecting line in conjunction with a pumping means produces a pressure gradient between the interior of the vacuum chamber and the interior of the measurement chamber. One end of the connecting line, located within the vacuum chamber terminates directly in the central region of a cathode surfaces facing the interior of the chamber, and the length, as well as the cross section of the connecting line being dimensioned so that the pressure gradient between the interior of the vacuum chamber and the interior of the measurement chamber, is precisely that required for measurement.

The invention is used for the determination of gas concentration of a gas in chamber, such as a vacuum-coating chamber for a PVD (plasma vapor deposition) method with an arrangement of cathodes in the chamber, a gas inlet into the chamber, a separate measurement chamber for accommodating a measuring device, which preferably is a measuring head of a mass spectrometer, a connecting line between the two chambers with a pressure stage, as well as a pump stand for producing a pressure gradient between the interior of the vacuum chamber and the interior of the measurement chamber.
A method and an apparatus are known for the reactive deposition of a metal compound on a substrate (European patent 0 121 019). For this method, the amount of reactive gas in the chamber is determined by withdrawing the excess reactive gas in the chamber at a place, which is spatially removed from the reaction zone. This publication furthermore describes an apparatus, which contains a mass spectrometer, which generates a control command. An adjustable control circuit is connected to the mass spectrometer in order to generate a specific peak signal from the different signals initially generated by the mass spectrometer.
An apparatus for determining the concentration of a gas in a vacuum chamber is well known in the art. By means of a pressure stage, this apparatus disengages a portion of the gas atmosphere of a vacuum chamber at a pressure of between, for example, 1.times.10.sup.-3 mbar and 5.times.10.sup.-2 mbar and generates before the measuring head of a mass spectrometer a pressure preferably of between 1.times.10.sup.-5 mbar and 5.times.10.sup.-4 mbar. The pressure stage consists, for example, of an aperture, which is pumped differentially with a separate pumping means.
The reactive gas concentration, which has a critical effect on the coating result of the vacuum deposition process, for example, in the case of a PVD (plasma vapor deposition) method, is that located immediately in front of, or in contact with the target surface. On the other hand, previously known state of the art methods consider only a composition of the gas atmosphere which exists at some distance from the cathode. Due to an inhomogeneous gas distribution at or near the surfaces at which the reaction takes place, the chemical composition at some distance from the cathode can differ substantially from the chemical composition of interest. Furthermore, because of the characteristics of the measuring equipment, a higher proportion of the reactive gas in the atmosphere to be measured would likewise improve the signal-to-noise ratio of the signal at the mass spectrometer.
For these reasons, an atmosphere, which is as close as possible to the cathode surface, should be made available to the mass spectrometer for analysis.

Apparatus for determining the concentration of a gas in a vacuum chamber


A cutting tool including a substrate composed of a silicon nitride-based sintered body and a coating layer. The coating layer includes first, second, third and fourth layers. The first layer is located on the surface of the substrate and is composed of TiN having an average crystalline width of 0.1 to 0.4 μm. The second layer is located on the first layer and composed of Al2O3 having an average crystalline width of 0.01 to 1.5 μm. The third layer is located on the second layer and is composed of TiN having an average crystalline width of 0.01 to 0.1 μm which is smaller than that of the first layer. The fourth layer is located on the third layer and is composed of Al2O3 having an average crystalline width of 0.01 to 1.5 μm.

Among cutting tools widely used for cutting of metals, printed circuit boards, and the like, a cutting tool has been known which includes a substrate composed of a cemented carbide, a cermet, a ceramic, or the like and a single or a multiple coating layer provided on a surface of the substrate. As this coating layer, a chemical vapor deposition (CVD) film including a TiC (titanium carbide) layer, a TiN (nitride titanium) layer, a TiCN (titanium carbonitride) layer, an Al2O3 (aluminum oxide) layer, and the like laminated to each other has been used in many cases.
For example, Patent Literature 1 has disclosed a cutting insert in which a surface of a cemented carbide substrate is coated with a TiCN layer, an Al2O3 layer, and a TiC layer which are laminated in this order.
In addition, Patent Literature 2 has disclosed the structure in which a surface of a silicon nitride substrate is coated with a hard coating layer, and a first layer of the hard coating layer is a titanium nitride layer of a columnar crystal which contains a granular crystal having a crystalline grain diameter of 1 to 30 nm.
The following is a list of the aforementioned background art    PTL 1: Japanese Unexamined Patent Application Publication No. 2003-213455    PTL 2: Japanese Unexamined Patent Application Publication No. 10-015707
However, even by the structure of the coating layer disclosed in Japanese Unexamined Patent Application Publication No. 2003213455 which is composed by repeatedly laminating a TiCN (TiN) layer and an Al2O3 layer, or even by the structure as disclosed in Japanese Unexamined Patent Application Publication No. 10-015707 in which the crystalline form of a TiN layer provided right over a substrate composed of a silicon nitride-based sintered body includes both a granular crystal and a columnar crystal at a portion adjacent to the substrate, the adhesion of the coating layer was not sufficient in some cases, and in addition, chipping thereof occurred in some cases due to cracks generated in an Al2O3 layer in cutting.
The present invention was made to overcome the problems described above, and an object of the present invention is to provide a cutting tool including a coating layer which has a high adhesion and high chipping-resistant characteristics.


A magnetoresistive sensor in the magnetic storage device includes a magnetoresistive element for sensing magnetic fields carried on storage medium. A cooling device is thermally coupled to the magnetoresistive element and arranged to conduct heat in a direction away from the magnetoresistive element to thereby cool the magnetoresistive element during normal operation of the storage device.

The present invention relates to magnetic storage systems. More specifically, the present invention relates to magnetoresistive sensors used in magnetic storage systems to read back stored information.
Magnetic storage systems are used to store information in a magnetic format for subsequent retrieval. Various technologies are available for reading back the stored information. One promising technology is the use of a magnetoresistive sensor for such read back.
Magnetoresistive sensors are responsive to a change in resistivity caused by the presence of magnetic fields and are increasingly being employed as read back elements in the heads of magnetic disc drives. They are particularly advantageous because the change in resistivity is independent of disc speed and depends only on the magnetic flux. Further, the sensor output is easily scaled by adjusting the sense current.
Magnetoresistive sensors typically comprise a thin strip of ferromagnetic material such as NiFe magnetized along an easy axis of low coercivity. The strip is mounted in the head such that the easy axis is transverse to the direction of disc rotation and parallel to the plane of the disc. Magnetic flux from the disc surface causes rotation of the magnetization vector of the stip, which in turn causes a change in electrical resistivity. A sense current is passed through the thin strip and the magnetic flux can be detected by measuring the change in voltage across the element as a result of the changing resistivity.
One drawback with magnetoresistive sensors is that the resistance of the magnetoresistive element and its sensitivity vary with temperature. Magnetoresistive sensor parametrics have been measured to degrade at ambient temperatures as low as 65.degree. C. However, during operation of the magnetoresistive sensor, its temperature tends to rise due, to for example, current flowing through the sense element, and the general increase in the ambient temperature from other components in the disc drive system such as the spindle motor, actuator and control circuitry. Common operating temperatures are as high as 60.degree. C. during normal operation, or even higher during intense operation. Further, the ambient temperature for high end components is being pushed higher as higher speeds, increased seek rates, and higher density components are designed. The prior art has focused on making the magnetoresistive sensor element less heat sensitive and/or attempting to compensate for this temperature dependency.

Magnetoresistive sensor for magnetic storage system


A solid-state imaging device includes, in a semiconductor substrate, a pixel portion provided with a photoelectric conversion portion, which photoelectrically converts incident light to obtain an electric signal and a peripheral circuit portion disposed on the periphery of the pixel portion, wherein a gate insulating film of aMOS transistor in the peripheral circuit portion is composed of a silicon oxynitride film, a gate insulating film of aMOS transistor in the pixel portion is composed of a silicon oxynitride film, and an oxide film is disposed just above the photoelectric conversion portion in the pixel portion.

1. Field of the Invention
The present invention relates to a solid-state imaging device, a method for manufacturing the solid-state imaging device, and an imaging apparatus.
2. Description of the Related Art
Regarding a solid-state imaging device, e.g., a CMOS sensor, including a pixel portion provided with a photoelectric conversion portion, which photoelectrically converts incident light to obtain an electric signal, and a peripheral circuit portion disposed on the periphery of the pixel portion, in a semiconductor substrate, a gate insulating film of the peripheral circuit portion (logic element portion) has become thinner as the element has become finer. Along with that, an increase in tunnel current of the gate insulating film becomes a problem. In the MOS transistor technology, a silicon oxynitride film is used as the gate insulating film in order to suppress a tunnel current of the gate insulating film (refer to, for example, Japanese Patent No. 3752241).
In the case where a logic transistor, which includes a silicon oxynitride film serving as a gate insulating film of an element (MOS transistor) disposed in the peripheral circuit portion of the CMOS sensor, is applied, it is desirable that the performance of the CMOS sensor does not deteriorate.
In addition, as shown in FIG. 46, if a gate insulating film 31 composed of a silicon oxynitride film remains on a photoelectric conversion portion (for example, photodiode) 21, there is a problem in that deterioration in white defect occurs because of a fixed charge in the gate insulating film 31.
Furthermore, as shown in FIG. 47, regarding an antireflection film just above a photoelectric conversion portion (for example, photodiode) 21, since a three layer structure (not shown in the drawing) of silicon oxide film/silicon nitride film/silicon oxide film becomes a multiple structure of silicon oxide (SiO2) film/silicon nitride (SiN) film/silicon oxide (SiO2) film/silicon oxynitride film, the light undergoes multiple reflection and the ripple property in dispersion of light deteriorates. Moreover, since the ripple property deteriorates, a problem occurs in that variations in dispersion of light increase between chips.
In addition, there is a problem in that optimization becomes complicated because of a multiple structure.

Solid-state imaging device, method for manufacturing solid-state imaging device, and imaging apparatus


In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. A Bernoulli Process Topic (BPT) model is provided which models the corpus at two levels: document level and citation level. In the BPT model, each document has two different representations in the latent topic space associated with its roles. Moreover, the multi-level hierarchical structure of the citation network is captured by a generative process involving a Bernoulli process. The distribution parameters of the BPT model are estimated by a variational approximation approach.

Unsupervised learning from documents is a fundamental problem in machine learning, which aims at modeling the documents and providing a meaningful description of the documents while preserving the basic statistical information about the corpus. Many learning tasks, such as organizing, clustering, classifying, or searching a collection of the documents, fall into this category. This problem becomes even more important with the existing huge repositories of text data, especially with the rapid development of Internet and digital databases, and thus receives an increasing attention recently.
There has been comprehensive research on the unsupervised learning from a corpus and the latent topic models play a central role among the existing methods. The topic models extract the latent topics from the corpus and therefore represent the documents in the new latent semantic space. This new latent semantic space bridges the gap between the documents and words and thus enables the efficient processing of the corpus such as browsing, clustering, and visualization.
One of the learning tasks which play central roles in the data mining field is to understand the content of a corpus such that one can efficiently store, organize, and visualize the documents. Moreover, it is essential in developing the human-machine interface in an information processing system to improve user experiences. This problem has received more and more attentions recently since huge repositories of documents are made available by the development of the Internet and digital databases and analyzing such large-scale corpora is a challenging research area. Among the numerous approaches on the knowledge discovery from documents, the latent topic models play an important role. The topic models extract latent topics from the corpus and the documents have new representations in the new latent semantic space. This new latent semantic space bridges the gap between the documents and the words and thus enables efficient processing of the corpus such as browsing, clustering, and visualization. Probabilistic Latent Semantic Indexing (PLSI) [T. Hofmann, "Probabilistic latent semantic indexing," in SIGIR, 1999, pp. 50-57.] and Latent Dirichlet Allocation (LDA) [D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," in Journal of Machine Learning Research, 2003, pp. 993-1022.] are two well-known topic models.
PLSI (Hofmann 1999) and LDA (Blei, Ng, and Jordan 2003) are two well known topic models toward document modeling by treating each document as a mixture of a set of topics. In these and other existing probabilistic models, a basic assumption underpinning the generative process is that the documents are independent of each other. More specifically, they assume that the topic distributions of the documents are independent of each other. However, this assumption does not hold true in practice and the documents in a corpus are actually related to each other in certain ways; for example, research papers are related to each other by citations. The existing approaches treat the citations as the additional features similar to the content. For example, Cohn et al. (2000) applies the PLSI model to a new feature space which contains both content and citations. The LDA model is also exploited in a similar way (Erosheva, Fienberg, and Lafferty 2004). As another example, Zhu et al. (2007) combine the content and citations to form an objective function for optimization.
A basic assumption underpinning the PLSI and LDA models as well as other topic models is that the documents are independent of each other. However, documents in most of corpora are related to each other in many ways instead of being isolated, which suggests that such information should be considered in analyzing the corpora. For example, research papers are related to each other by citations in the digital libraries. One approach is to treat the citations as the additional features in a similar way to the content features and apply the existing approaches to the new feature space, where Cohn et al. [D. A. Cohn and T. Hofmann, "The missing link—a probabilistic model of document content and hypertext connectivity," in NIPS, 2000, pp. 430-436] used PLSI model and Erosheva et al. [E. Erosheva, S. Fienberg, and J. Lafferty, "Mixed membership models of scientific publications," in Proceedings of the National Academy of Sciences, 101 Suppl 1:5220-7 (2004)] applied LDA model. Zhu et al. [S. Zhu, K. Yu, Y. Chi, and Y. Gong, "Combining content and link for classification using matrix factorization," in SIGIR, 2007, pp. 487-494] formulated a loss function in the new feature space for optimization. The above studies, however, fail to capture two important properties of the citation network. First, one document plays two different roles in the corpus: document itself and a citation of other documents. The topic distributions of these two roles are different and are related in a particular way. It should be beneficial to model the corpus at a finer level by differentiating these two roles for each document. For example, in the well-known LDA paper, Blei et al. [D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," in Journal of Machine Learning Research, 2003, pp. 993-1022] proposed a graphical model for document modeling and adopted the variational inference approach for parameter estimation. When the LDA paper serves as the citation role, one might be more interested in the graphical model and variational inference approach than other content covered in the LDA paper. This is the case, especially when one is interested in the applications of the LDA model in other contexts, such as the document clustering task. Therefore, the topic distributions of the LDA paper at the two levels (document level and citation level) are different, as illustrated in FIG. 1. The topic models which simply treat the citations as the features in a peer-level to the content fail to differentiate these two levels.
The second property of the citation network that is ignored by the above studies is the multi-level hierarchical structure, which implies that the relations represented by the citations are transitive. A small citation network is illustrated in FIG. 2, where the first level citations of document d1 are those papers directly cited by d1 and the second level citations of d1 are those papers cited by the papers in the reference list of d1. Although the second level citations are not directly cited by d1, they are also likely to influence d1 to a lesser degree than the first level citations. For example, d5 is not directly cited by d1; however, d1 is probably influenced by d5 indirectly through d2. A topic model which fails to capture such multi-level structure is flawed.
The Latent Dirichlet allocation (LDA) (see, Blei, David and Lafferty, John, "Topic Models", In A. Srivastava and M. Sahami, editors, Text Mining: Theory and Applications. Taylor and Francis, 2009, expressly incorporated by reference, and liberally quoted below), has is a basis for many other topic models. LDA is based on latent semantic indexing (LSI) (Deerwester et al., 1990) and probabilistic LSI (Hofmann, 1999). See also, Steyvers and Griffiths (2006). LDA can be developed from the principles of generative probabilistic models. LDA models documents as arising from multiple topics, where a topic is defined to be a distribution over a fixed vocabulary of terms. Specifically, we assume that K topics are associated with a collection, and that each document exhibits these topics with different proportions. Documents in a corpus tend to be heterogeneous, combining a subset of main ideas or themes from the collection as a whole. These topics are not typically known in advance, but may be learned from the data.
More formally, LDA provides a hidden variable model of documents. Hidden variable models are structured distributions in which observed data interact with hidden random variables. With a hidden variable model, a hidden structure is posited within in the observed data, which is inferred using posterior probabilistic inference. Hidden variable models are prevalent in machine learning; examples include hidden Markov models (Rabiner, 1989), Kalman filters (Kalman, 1960), phylogenetic tree models (Mau et al., 1999), and mixture models (McLachlan and Peel, 2000).
In LDA, the observed data are the words of each document and the hidden variables represent the latent topical structure, i.e., the topics themselves and how each document exhibits them. Given a collection, the posterior distribution of the hidden variables given the observed documents determines a hidden topical decomposition of the collection. Applications of topic modeling use posterior estimates of these hidden variables to perform tasks such as information retrieval and document browsing.
The relation between the observed documents and the hidden topic structure is extracted with a probabilistic generative process associated with LDA, the imaginary random process that is assumed to have produced the observed data. That is, LDA assumes that the document is randomly generated based on the hidden topic structure.
Let K be a specified number of topics, V the size of the vocabulary, {right arrow over (α)} a positive K-vector, and η a scalar. DirV ({right arrow over (α)}) denotes a V-dimensional Dirichlet with vector parameter {right arrow over (α)} and DirK (η) denote a K dimensional symmetric Dirichlet with scalar parameter η. For each topic, we draw a distribution over words {right arrow over (β)}k˜DirV (η). For each document, we draw a vector of topic proportions {right arrow over (θ)}d˜DirV ({right arrow over (α)}). For each word, we draw a topic assignment Zd,n˜Mult({right arrow over (θ)}d), Zd,nε{1, . . . , K}, and draw a word Wd,n˜Mult({right arrow over (β)}zd,n), Wd,nε{1, . . . , V}. This process is illustrated as a directed graphical model in FIG. 9.
The hidden topical structure of a collection is represented in the hidden random variables: the topics {right arrow over (β)}1:K, the per-document topic proportions {right arrow over (θ)}1:D, and the per-word topic assignments z1:D,1:N. With these variables, LDA is a type of mixed-membership model (Erosheva et al., 2004). These are distinguished from classical mixture models (McLachlan and Peel, 2000; Nigam et al., 2000), where each document is limited to exhibit one topic.
This additional structure is important because documents often exhibit multiple topics; LDA can model this heterogeneity while classical mixtures cannot. Advantages of LDA over classical mixtures has been quantified by measuring document generalization (Blei et al., 2003). LDA makes central use of the Dirichlet distribution, the exponential family distribution over the simplex of positive vectors that sum to one. The Dirichlet has density:
      p              (              θ        |                  α          ->                    )        =                    Γ        (                              ∑            i                                          α            i                          )                              ∏          i                                                                    Γ                                (                          α              i                        )                                                  ∏        i                                                              θ          i                                    α              i                        -            1                          .
The parameter {right arrow over (α)} is a positive K-vector, and Γ denotes the Gamma function, which can be thought of as a real-valued extension of the factorial function. A symmetric Dirichlet is a Dirichlet where each component of the parameter is equal to the same value. The Dirichlet is used as a distribution over discrete distributions; each component in the random vector is the probability of drawing the item associated with that component.
LDA contains two Dirichlet random variables: the topic proportions {right arrow over (θ)} are distributions over topic indices {1, . . . , K}; the topics {right arrow over (β)} are distributions over the vocabulary.
Exploring a corpus with the posterior distribution. LDA provides a joint distribution over the observed and hidden random variables. The hidden topic decomposition of a particular corpus arises from the corresponding posterior distribution of the hidden variables given the D observed documents {right arrow over (w)}1:D,
      p              (                                    θ            →                                              1              :              D                        ,                                          z                1                            :              D                        ,                          1              :              N                                      ,                                            β              →                                      1              :              K                                |                      w                                          1                :                D                            ,                              1                :                N                                                    ,        α        ,        η            )        =            p                    (                                            θ              →                                                      1                :                D                            ,                                                                    z                    →                                    1                                :                D                            ,                              1                :                N                                              ,                                                    β                →                                            1                :                K                                      |                                          w                →                                                              1                  :                  D                                ,                                  1                  :                  N                                                              ,          α          ,          η                )                            ∫                              β            →                                1            :            K                                                      ∫                                    θ              →                                      1              :              D                                                                    ∑                          Z              →                                                      p                                      (                                                                    θ                    →                                                                              1                      :                      D                                        ,                                                                                z                          →                                                1                                            :                      D                                                                      ,                                                                            β                      →                                                              1                      :                      K                                                        |                                                            w                      →                                                              1                      :                      D                                                                      ,                α                ,                η                            )
Loosely, this posterior can be thought of the "reversal" of the generative process described above. Given the observed corpus, the posterior is a distribution of the hidden variables which generated it.
Computing this distribution is generally considered intractable because of the integral in the denominator, Blei et al. (2003). The posterior distribution gives a decomposition of the corpus that can be used to better understand and organize its contents. The quantities needed for exploring a corpus are the posterior expectations of the hidden variables. These are the topic probability of a term {circumflex over (β)}k,v=E[βk,v|w1:D,1:N], the topic proportions of a document {circumflex over (θ)}d,k=E[θd,k|w1:D,1:N], and the topic assignment of a word {circumflex over (z)}d,n,k=E[Zd,n=k|w1:D,1:N]. Note that each of these quantities is conditioned on the observed corpus.
Exploring a corpus through a topic model typically begins with visualizing the posterior topics through their per-topic term probabilities {circumflex over (β)}. The simplest way to visualize a topic is to order the terms by their probability. However, we prefer the following score,
      term              -                  score              k        ,        v              =                    β        ^                    k        ,        v                                log        (                                            β              ^                                      k              ,              v                                                          (                                                ∏                                      j                    =                    1                                    K                                                                                β                    ^                                                        k                    ,                    v                                                              )                                      1              K                                      )            .
This is inspired by the popular TFIDF term score of vocabulary terms used in information retrieval Baeza-Yates and Ribeiro-Neto (1999). The first expression is akin to the term frequency; the second expression is akin to the document frequency, down-weighting terms that have high probability under all the topics. Other methods of determining the difference between a topic and others can be found in (Tang and MacLennan, 2005).
The posterior topic proportions {circumflex over (θ)}d,k and posterior topic assignments to {circumflex over (z)}d,n,k to visualize the underlying topic decomposition of a document. Plotting the posterior topic proportions gives a sense of which topics the document is "about." These vectors can also be used to group articles that exhibit certain topics with high proportions. Note that, in contrast to traditional clustering models (Fraley and Raftery, 2002), articles contain multiple topics and thus can belong to multiple groups. Finally, examining the most likely topic assigned to each word gives a sense of how the topics are divided up within the document.
The posterior topic proportions can be used to define a topic-based similarity measure between documents. These vectors provide a low dimensional simplicial representation of each document, reducing their representation from the (V−1)-simplex to the (K−1)-simplex. One can use the Hellinger distance between documents as a similarity measure,
      document              -                  similarity              d        ,        f              =            ∑              k        =        1            K                                    (                                                                      θ                  ^                                                  d                  ,                  k                                                      -                                                            θ                  ^                                                  f                  ,                  k                                                              )                2            .
The central computational problem for topic modeling with LDA is approximating the posterior. This distribution is the key to using LDA for both quantitative tasks, such as prediction and document generalization, and the qualitative exploratory tasks that we discuss here. Several approximation techniques have been developed for LDA, including mean field variational inference (Blei et al., 2003), collapsed variational inference (Teh et al., 2006), expectation propagation (Minka and Lafferty, 2002), and Gibbs sampling (Steyvers and Griffiths, 2006). Each has advantages and disadvantages: choosing an approximate inference algorithm amounts to trading off speed, complexity, accuracy, and conceptual simplicity.
The basic idea behind variational inference is to approximate an intractable posterior distribution over hidden variables, with a simpler distribution containing free variational parameters. These parameters are then fit so that the approximation is close to the true posterior.
The LDA posterior is intractable to compute exactly because the hidden variables (i.e., the components of the hidden topic structure) are dependent when conditioned on data. Specifically, this dependence yields difficulty in computing the denominator of the posterior distribution equation, because one must sum over all configurations of the interdependent N topic assignment variables z1:N.
In contrast to the true posterior, the mean field variational distribution for LDA is one where the variables are independent of each other, with and each governed by a different variational parameter:
      q              (                                    θ            →                                              1              :              D                        ,                                          z                1                            :              D                        ,                          1              :              N                                      ,                              β            →                                1            :            K                              )        =            ∏              k        =        1            K                                            q                          (                                                    β                →                            k                        |                                          λ                →                            k                                )                                            ∏                      d            =            1                    D                                                                    (                                    q                                            (                                                                            θ                      →                                        dd                                    |                                                            γ                      →                                        d                                                  )                                                                                ∏                                  n                  =                  1                                N                                                                                q                                                  (                                                            z                                              d                        ,                        n                                                              |                                                                  ϕ                        →                                                                    d                        ,                        n                                                                              )                                                              )
Each hidden variable is described by a distribution over its type: the topics {right arrow over (β)}1:K are each described by a V-Dirichlet distribution {right arrow over (λ)}k; the topic proportions {right arrow over (θ)}1:D are each described by a K-Dirichlet distribution {right arrow over (λ)}d; and the topic assignment zd,n is described by a K-multinomial distribution {right arrow over (φ)}d,n. In the variational distribution these variables are independent; in the true posterior they are coupled through the observed documents. The variational parameters are fit to minimize the Kullback-Leibler (KL) to the true posterior:
  arg                        min                                    γ            →                                1            :            D                          ,                              λ            →                                1            :            K                          ,                              ϕ            →                                              1              :              D                        ,                          1              :              N                                                                        KL      (                        q                                (                                                            θ                  →                                                                      1                    :                    D                                    ,                                                            z                      1                                        :                    D                                    ,                                      1                    :                    N                                                              ,                                                β                  →                                                  1                  :                  K                                                      )                                                                  p                                      (                                                                    θ                    →                                                                              1                      :                      D                                        ,                                                                  z                        1                                            :                      D                                        ,                                          1                      :                      N                                                                      ,                                                                            β                      →                                                              1                      :                      K                                                        |                                      w                                                                  1                        :                        D                                            ,                                              1                        :                        N                                                                                )                                )
The objective cannot be computed exactly, but it can be computed up to a constant that does not depend on the variational parameters. (In fact, this constant is the log likelihood of the data under the model.)
Specifically, the objective function is
      ℒ                      ∑                  k          =          1                K                          E                          [                      log                                                                                p                                            (                                                                            β                      →                                        k                                    |                  η                                )                                              ]                      +            ∑              d        =        1            D                  E                    [                  log                                                                          p                                      (                                                                    θ                    →                                    d                                |                                  α                  →                                            )                                      ]              +            ∑              d        =        1            D                          ∑                  k          =          1                K                          E                          [                      log                                                                                p                                            (                                                      Z                                          d                      ,                      n                                                        |                                                            θ                      →                                        d                                                  )                                              ]                      +            ∑              d        =        1            D                          ∑                  k          =          1                K                          E                          [                      log                                                                                p                                            (                                                                            w                                              d                        ,                        n                                                              |                                          Z                                              d                        ,                        n                                                                              ,                                                            β                      →                                                              1                      :                      K                                                                      )                                              ]                      +      H              (      q      )      where H denotes the entropy and all expectations are taken with respect to the variational parameter distribution. See Blei et al. (2003) for details on how to compute this function. Optimization proceeds by coordinate ascent, iteratively optimizing each variational parameter to increase the objective. Mean field variational inference for LDA is discussed in detail in (Blei et al., 2003), and good introductions to variational methods include (Jordan et al., 1999) and (Wainwright and Jordan, 2005).
The true posterior Dirichlet variational parameter for a term given all of the topic assignments and words is a Dirichlet with parameters η+nk,w, where nk,w denotes the number of times word w is assigned to topic k. (This follows from the conjugacy of the Dirichlet and multinomial. See (Gelman et al., 1995) for a good introduction to this concept.) The update of λ below is nearly this expression, but with nk,w replaced by its expectation under the variational distribution. The independence of the hidden variables in the variational distribution guarantees that such an expectation will not depend on the parameter being updated. The variational update for the topic proportions γ is analogous.
The variational update for the distribution of zd,n follows a similar formula. Consider the true posterior of zd,n, given the other relevant hidden variables and observed word wd,n,p(zd,n=k|{right arrow over (θ)}d,wd,n,{right arrow over (β)}1:K)∝exp{log θd,k+log βk,wd,n}
The update of φ is this distribution, with the term inside the exponent replaced by its expectation under the variational distribution. Note that under the variational Dirichlet distribution, E[log βk,w]=Ψ(λk,w)−Ψ(τvλk,v), and E[log θd,k] is similarly computed.
An iteration of mean field variational inference for LDA is provided as follows:
(1) For each topic k and term v:
      λ          k      ,      v              (              t        +        1            )        =      η    =                  ∑                  d          =          1                D                                    ∑                      n            =            1                    N                                  1                                (                                          w                                  d                  ,                  n                                            =              v                        )                                                        ϕ                              n                ,                k                                            (                t                )                                      .
(2) For each document d:                (a) Update γd γd,k(t+1)=αk+Σn=1Nφd,n,k(t)         (b) For each word n, update {right arrow over (φ)}d,n:φd,n,k(t+1)∝exp{Ψ(γd,k(t+1))+Ψ(λk,wn(t+1))−Ψ(Σv=1Vλk,v(t+1)}where Ψ is the digamma function, the first derivative of the log Γ function.
This algorithm is repeated until the objective function converges. Each update has a close relationship to the true posterior of each hidden random variable conditioned on the other hidden and observed random variables.
This general approach to mean-field variational methods—update each variational parameter with the parameter given by the expectation of the true posterior under the variational distribution—is applicable when the conditional distribution of each variable is in the exponential family. This has been described by several authors (Beal, 2003; Xing et al., 2003; Blei and Jordan, 2005) and is the backbone of the VIBES framework (Winn and Bishop, 2005). The quantities needed to explore and decompose the corpus are readily computed from the variational distribution.
The per-term topic probabilities are:
            β      ^              k      ,      v        =                    λ                  k          ,          v                                      ∑                                    v              ′                        =            1                    V                                  λ                      k            ,                          v              ′                                            .
The per topic proportions are:
            θ      ^              d      ,      v        =                    γ                  d          ,          v                                      ∑                                    k              ′                        =            1                    K                                  γ                      d            ,                          k              ′                                            .
The per topic assignment expectation is: {circumflex over (z)}d,n,k=φd,n,k.
The computational bottleneck of the algorithm is typically computing the Ψ function, which should be precomputed as much as possible.
Each of the correlated topic model and the dynamic topic model embellishes LDA to relax one of its implicit assumptions. In addition to describing topic models that are more powerful than LDA, the practice of topic modeling is also considered. Deciding on an appropriate model of a corpus depends both on what kind of structure is hidden in the data and what kind of structure the practitioner cares to examine. While LDA may be appropriate for learning a fixed set of topics, other applications of topic modeling may call for discovering the connections between topics or modeling topics as changing through time.
The correlated topic model addresses one limitation of LDA, which fails to directly model correlation between the occurrence of topics. In many text corpora, it is natural to expect that the occurrences of the underlying latent topics will be highly correlated. In LDA, this modeling limitation stems from the independence assumptions implicit in the Dirichlet distribution of the topic proportions. Specifically, under a Dirichlet, the components of the proportions vector are nearly independent, which leads to the strong assumption that the presence of one topic is not correlated with the presence of another. (We say "nearly independent" because the components exhibit slight negative correlation because of the constraint that they have to sum to one.)
In the correlated topic model (CTM), the topic proportions are modeled with an alternative, more flexible distribution that allows for covariance structure among the components (Blei and Lafferty, 2007). This gives a more realistic model of latent topic structure where the presence of one latent topic may be correlated with the presence of another. The CTM better fits the data, and provides a rich way of visualizing and exploring text collections.
The key to the CTM is the logistic normal distribution (Aitchison, 1982). The logistic normal is a distribution on the simplex that allows for a general pattern of variability between the components. It achieves this by mapping a multivariate random variable from Rd to the d-simplex. In particular, the logistic normal distribution takes a draw from a multivariate Gaussian, exponentiates it, and maps it to the simplex via normalization. The covariance of the Gaussian leads to correlations between components of the resulting simplicial random variable. The logistic normal was originally studied in the context of analyzing observed data such as the proportions of minerals in geological samples. In the CTM, it is used in a hierarchical model where it describes the hidden composition of topics associated with each document.
Let {μ,Σ} be a K-dimensional mean and covariance matrix, and let topics β1:K be K multinomials over a fixed word vocabulary, as above. The CTM assumes that an N-word document arises from the following generative process:
(1) Draw η|{μ, Σ}˜n(μ, Σ}.
(2) For nε{1, . . . , N}                a. Draw a topic assignment Zn|η from Mult(ƒ(η)).        b. Draw word Wn|{(zn, β1:K} from Mult(βzn)
The function that maps the real-vector η to the simplex is
      f              (              η        i            )        =            exp                    {                  η          i                }                            ∑        j                          exp                          {                      η            j                    }
Note that this process is identical to the generative process of LDA except that the topic proportions are drawn from a logistic normal rather than a Dirichlet. The model is shown as a directed graphical model in FIG. 9.
The CTM is more expressive than LDA because the strong independence assumption imposed by the Dirichlet in LDA is not realistic when analyzing real document collections. Quantitative results illustrate that the CTM better fits held out data than LDA (Blei and Lafferty, 2007). Moreover, this higher order structure given by the covariance can be used as an exploratory tool for better understanding and navigating a large corpus. The added flexibility of the CTM comes at a computational cost. Mean field variational inference for the CTM is not as fast or straightforward as the algorithm described above for Analyzing an LDA. In particular, the update for the variational distribution of the topic proportions must be fit by gradient-based optimization. See (Blei and Lafferty, 2007) for details.
LDA and the CTM assume that words are exchangeable within each document, i.e., their order does not affect their probability under the model. This assumption is a simplification that it is consistent with the goal of identifying the semantic themes within each document. But LDA and the CTM further assume that documents are exchangeable within the corpus, and, for many corpora, this assumption is inappropriate. The topics of a document collection evolve over time. The evolution and dynamic changes of the underlying topics may be modeled. The dynamic topic model (DTM) captures the evolution of topics in a sequentially organized corpus of documents. In the DTM, the data is divided by time slice, e.g., by year. The documents of each slice are modeled with a K-component topic model, where the topics associated with slice t evolve from the topics associated with slice t−1.
The logistic normal distribution is also exploited, to capture uncertainty about the time-series topics. The sequences of simplicial random variables are modeled by chaining Gaussian distributions in a dynamic model and mapping the emitted values to the simplex. This is an extension of the logistic normal to time-series simplex data (West and Harrison, 1997).
For a K-component model with V terms, let {right arrow over (π)}t,k denote a multivariate Gaussian random variable for topic k in slice t. For each topic, we chain {{right arrow over (π)}1,k, . . . {right arrow over (π)}T,k} in a state space model that evolves with Gaussian noise: {right arrow over (π)}t,k|{right arrow over (π)}t-1,k˜N({right arrow over (π)}t-1,k,σ2I).
When drawing words from these topics, the natural parameters are mapped back to the simplex with the function ƒ. Note that the timeseries topics use a diagonal covariance matrix. Modeling the full V×V covariance matrix is a computational expense that is not necessary for this purpose.
By chaining each topic to its predecessor and successor, a collection of topic models is sequentially tied. The generative process for slice t of a sequential corpus is
(1) Draw topics {right arrow over (π)}t,k|{right arrow over (π)}t-1,k˜N({right arrow over (π)}t-1,k,σ2I)
(2) For each document:                a. Draw θd˜Dir({right arrow over (α)})        b. For each word:                    i. Draw Z˜Mult(θd)            ii. Draw Wt,d,n˜Mult(ƒ({right arrow over (π)}t,z)).
This is illustrated as a graphical model in FIG. 10. Notice that each time slice is a separate LDA model, where the kth topic at slice t has smoothly evolved from the kth topic at slice t−1.
The posterior can be approximated over the topic decomposition with variational methods (see Blei and Lafferty (2006) for details). At the topic level, each topic is now a sequence of distributions over terms. Thus, for each topic and year, we can score the terms (termscore) and visualize the topic as a whole with its top words over time, providing a global sense of how the important words of a topic have changed through the span of the collection. For individual terms of interest, their score may be examined over time within each topic. The overall popularity of each topic is examined from year to year by computing the expected number of words that were assigned to it.
The document similarity metric (document-similarity) has interesting properties in the context of the DTM. The metric is defined in terms of the topic proportions for each document. For two documents in different years, these proportions refer to two different slices of the K topics, but the two sets of topics are linked together by the sequential model. Consequently, the metric provides a time corrected notion of document similarity.

Knowledge discovery from citation networks

The Research Foundation for The State University of New York


A removable cartridge configured to be inserted into a receiving channel. The cartridge includes an elongated body having a leading end, a trailing end, a top surface and a bottom surface. A button protrudes from the top surface of the elongated body and is positioned proximate the trailing end of the elongated body. When the cartridge is being inserted into the receiving channel, the button is compressed. After the cartridge is inserted into the receiving channel, the button releases into an opening that intersects with the receiving channel so as to lock the cartridge in the channel.

Spine boards are patient handling devices that provide rigid support to prevent further injury of a patient while the patient is being transported to a medical center for treatment. They are commonly used by medical personnel during emergency and rescue situations so as to provide increased efficiency and effectiveness in performing the vital tasks required.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.


A labeled compound is so designed that an aromatic tertiary amine compound is bondable with a biomolecule. One of S1 and S2 contains a group bound with a molecular chain 10 (e.g. an oligonucleotide) capable of binding with a biomolecule or a reactive group covalently binding with a reactive group present in the biomolecule, n is 0 or 1, R3 is a phenyl group or a naphthyl group, Ar1 is a phenylene group or a naphthylene group, and Ar2 is any of a phenylene group, a naphthylene group, an anthrylene group, and a phenanthrene group. The detection of a fluorescence emitted by excitation of the labeled compound bound to a biomolecule ensures the biomolecule being detected at high sensitivity and a high SN ratio.

The present disclosure relates to a labeled compound used for detection of a sample molecule and more particularly, to a labeled compound designed to permit an aromatic tertiary amine compound to be capable of binding with the sample molecule and also to a detection method using the same.
There are known techniques of analyzing biomolecules wherein sample molecules made of various types of biomolecules such as of nucleic acids, proteins, sugars, enzymes, hormones, antigens, antibodies and the like are fluorescently labeled and the fluorescence is detected. This analyzing technique is a fundamental one in the fields of the analysis of life phenomena, genetic diagnosis, tissue engineering and the like and is also an essential one in the screening of medical drugs. For instance, labeling of gene, protein with a fluorescent dye enables a specific type of gene or protein in an analyte to be detected at high sensitivity.
Comprehensive analyses of analytes have been conducted, for example, by labeling many types of oligonucleotides or peptides with fluorescent dyes and using DNA chips or protein chips fixing a microarray of the labeled oligonucleotides or peptides thereon. Moreover, in an immunostaining or molecule imaging technique wherein a specific type of biomolecule such as, for example, of a sugar, protein or the like is labeled with a fluorescent dye and is observed through a fluorescence microscope after generation of a fluorescence, dynamic analyses of an intracellular or intercellular molecule become possible.
The labeled probe obtained by labeling DNA, a DNA base derivative, a DNA oligonucleotide or the like with a fluorescent dye is employed in northern blotting, Southern blotting, in situ hybridization, DNA sequence determination reaction and the like. The antibody or antigen labeled with a fluorescent dye is used for immunoassay making use of an antigen-antibody reaction. There is also known an enzyme immunoassay method to which the avidin (streptoavidin)-biotin linkage is applied and in which avidin modified with a fluorescent dye is used.
For the labeling of a biomolecule serving as a sample to be detected, for example, an aromatic dye compound such as FITC (fluorescein isothiocyanate), TRITC (tetramethylrhodamine isothiocyanate), fluorescein, rhodamine, coumarin, cyanine dye or the like is used as the fluorescent labeling reagent.
Techniques relating to the detection of biomolecules and various types of fluorescent labeling compounds used for the detection of biomolecules are known and reported as shown below.
In Japanese Patent Laid-open No. 2005-208026 (at Paragraphs 0004-0011), entitled "Method of detecting biological molecules, and labeling dye and labeling kit used for the same," (referred to as Patent Document 1) there is the following description.
The method for detecting a biomolecule in the Patent Document 1 is characterized in that a biomolecule sample and an organic EL (electroluminescence) dye are reacted with each other and the fluorescence of the biomolecule sample labeled with the organic EL dye is measured. It is stated that the organic EL dye used is made of a compound containing a conjugated five-membered ring compound, for which there can be used a compound containing at least one hetero atom, selenium atom or boron atom. Moreover, there may be further used a condensed polycyclic compound made of a five-membered ring compound and a conjugated six-membered ring compound, wherein the five-membered ring compound used may be an azole derivative or an imidazole derivative.
Prior to the reaction with a biomolecule, the organic EL dye may be introduced thereinto with any one of functional groups selected from an isocyanate group, an epoxy group, a halogenated alkyl group, a triazine group, a carbodiimide group and an active-esterified carbonyl group.
The labeling dye of the Patent Document 1 is one used for detection of a biomolecule based on fluorescent measurement and is characterized in that it consists of an organic EL dye having a reactive group capable of binding to the biomolecule. It is stated that as the reactive group, there can be used any one of functional groups selected from a carboxylic group, an isocyanate group, an epoxy group, a halogenated alkyl group, a triazine group, a carbodiimide group and an active-esterified carbonyl group.
It is also stated in the Patent Document 1 that the organic EL dye is a compound containing a conjugated five-membered ring compound and a compound containing at least one hetero atom, selenium atom or boron atom may be used as this five-membered ring compound. In addition, there may be further used a condensed polycyclic compound composed of the five-membered ring compound and a conjugated six-membered ring compound. The five-membered ring compound may be an azole derivative or an imidazole derivative.
A variety of organic EL dyes are known aside from those set out in the Patent Document 1. For instance, there have been reported organic EL dyes composed of styryl compounds having a diversity of structures, for example, in Patent Documents indicated below:                Japanese Patent No. 3852517 (paragraphs 0017 to 0020),        Japanese Patent No. 3852518 (paragraphs 0017 to 0020),        Japanese Patent No. 3852520 (paragraphs 0015 to 0018),        Japanese Patent No. 3555736 (paragraphs 0017 to 0020),        Japanese Patent No. 3820752 (paragraphs 0017 to 0020),        Japanese Patent Laid-open No. 2000-230132 (paragraphs 0008 to 0021),        Japanese Patent Laid-open No. 2001-106657 (paragraphs 0008 to 0019),        Japanese Patent Laid-open No. 2001-106658 (paragraphs 0008 to 0012),        Japanese Patent Specification No. 3852552 (paragraphs 0017 to 0037),        Japanese Patent Laid-open No. 2001-131128 (paragraphs 0008 to 0012),        Japanese Patent Laid-open No. 2001-288377 (paragraphs 0007 to 0020),        Japanese Patent Laid-open No. 2002-226722 (paragraphs 0007 to 0014),        Japanese Patent Laid-open No. 2004-87463 (paragraphs 0021 to 0026),        Japanese Patent No. 4001118 (paragraphs 0013 to 0065),        Japanese Patent Laid-open No. 2005-5226 (paragraphs 0013 to 0057),        Japanese Patent Laid-open No. 2005-35927 (paragraphs 0013 to 0042),        Japanese Patent Laid-open No. 2006-124333 (paragraphs 0011 to 0054),        Japanese Patent Laid-open No. 2006-128437 (paragraphs 0009 to 0024), and        Japanese Patent Laid-open No. 2006-273737 (paragraphs 0009-0027).

Labeled compound and detection method using the same


A production method for a thermoelectric conversion module having a thermoelectric conversion element and an electrode, which are metallurgically bonded together via a porous metal layer. The porous metal layer is made of nickel or silver and has a density ratio of 50 to 90%.

1. Technical Field
The present invention relates to a thermoelectric conversion module that converts thermal energy to electric energy, and relates to a production method therefor. The thermoelectric conversion module has a bonding portion between a thermoelectric conversion element and an electrode, and thermal stress may occur in the bonding portion. Therefore, specifically, the present invention relates to a thermoelectric conversion module with a function to decrease the thermal stress, and relates to a production technique therefor.
2. Background Art
A power generation system provided with a thermoelectric conversion module using thermoelectric conversion elements directly generates electricity. This power generation system has a simple structure and does not have a movable part, thereby having high reliability and facilitating maintenance thereof. However, the power generation system has low power density and low energy conversion efficiency. Due to this, this power generation system has been developed only for special uses at low output scale, such as for use in space. However, in view of recent environmental issues, this power generation system is expected to be useful as an environmental protection measure. For example, this power generation system is anticipated to be useful as a small-scale distributed power generation system that uses an exhaust heat source of a waste incinerator, a cogeneration system, etc. This power generation system is also anticipated to be useful as an automobile power generation system that uses heat in exhaust gas of automobiles. Consequently, reduction in the cost of this power generation and improvement in durability of a thermoelectric conversion module system are required in this power generation system.
A thermoelectric conversion module is shown in FIG. 1. As shown in FIG. 1, the thermoelectric conversion module is constructed by stacking an electrode 2 on each side of a thermoelectric conversion element 1. Moreover, each of a cooling duct 4 and a heating duct 5 is stacked on the rest side of each electrode 2 via an electric insulating layer 3. The electrode 2 may be made of copper, and the electric insulating layer 3 may be made of mica. In this thermoelectric conversion module, by sending air to the cooling duct 4 and by supplying high-temperature exhaust gas to the heating duct 5, a temperature difference is generated between the two ends of the thermoelectric conversion element 1. The temperature difference generates thermoelectric power in the thermoelectric conversion element 1, whereby direct current is obtained from the electrode 2. Such a thermoelectric conversion module is disclosed in Japanese Patent Application of Laid-open No. 9-293906, for example.
In general, the thermoelectric conversion module is produced by pressing and bonding the thermoelectric conversion element and the electrodes, or by bonding them with a soldering material. As described above, the thermoelectric conversion module generates power based on the thermoelectric power. The thermoelectric power occurs by the temperature difference between the two ends of the thermoelectric conversion element. Therefore, when the temperature difference between the two ends of the thermoelectric conversion element is larger, the thermoelectric power is increased, and a greater amount of electricity is generated. In order to increase the temperature difference between the two ends of the thermoelectric conversion element, the temperature of the cooling side (cooling duct 4) may be decreased, but a special device is required, which is not preferable. Accordingly, usually the temperature of the heating side (heating duct 5) is increased to not more than an upper temperature limit of the thermoelectric conversion element.
The thermoelectric conversion element and the electrode at the heating side of the thermoelectric conversion module have a bonding portion therebetween. The thermoelectric conversion element does not greatly expand with the heat, but the electrode greatly expands with the heat. Therefore, in the bonding portion, there is a difference in the amounts of the thermal expansion of the thermoelectric conversion element and the electrode. As a result, the bonding portion receives stress due to the difference in the amounts of the thermal expansion. Accordingly, when the temperature of the heating side of the thermoelectric conversion element is increased so as to increase the amount of power generation, large thermal stress occurs in the bonding portion by the difference in the amounts of the thermal expansion. The large thermal stress easily causes fracture at the bonding portion between the thermoelectric conversion element and the electrode and its vicinity.
The thermoelectric conversion element and the electrode may be bonded with a soft soldering material. In this case, if the temperature of the heating side is set to be not less than the melt temperature of the soft soldering material, the soft soldering material is melted and leaks. Therefore, in this kind of thermoelectric conversion module, the temperature of the heating side is limited, and the amount of the power generation is limited.

Thermoelectric conversion module and production method therefor