tify the likelihood that the process assumes a particular shape. However, it is often more practical to characterize signal stochastics by means of its moments or, equivalently, its cumulants [Mid87]. If we are mainly interested in a stochastic signal's energy content, then it is sufficient to know all moments up to second order, i.e. the signal's expected value = =E{n(t)} and autocorrelation P(t,t)=E{n(t+t/2)n(t-t/2)}. = In order to cope with noise, any modeling framework for a particular class of building blocks must at least be able to characterize stochastic input-output behavior by means of the moments up to second order. Chapters 3, 4 and 5 outline how to perform noise computations for, respectively, LPTV systems and oscillators. 2.3 Conclusions Models are descriptions of systems that we want to realize or that we have realized. In a top-down design flow they are used to communicate design decisions between the different levels of hierarchy. For example, a frontend system engineer will pass an LNA specification model to a circuit engineer. It describes the LNA as the system engineer would like it to behave. For the circuit engineer, it makes up the starting point for circuit-level synthesis, resulting in an implementation model, e.g. a Spice netlist. This model describes the LNA as it is realized. It is used by the system engineer for frontend-level verification. In order for a top-down design flow to be successful, both specification and imple- mentation models must be as accurate as possible in capturing the behavior of actual on-chip implementations. Defective and incomplete models are one of the main rea- sons for a redesign to be required. Of course, at any given stage of the design process, one only disposes of the knowledge gathered up to that point in time. This includes ex- perience obtained from similar designs in the past. Models including all relevant prior knowledge and experience are called good models. The target of this work can there- fore be phrased as "constructing good models for telecom