# Modeling of subthreshold characteristics for double gate MOSFET using neural networks and genetic algorithm

E. N. Cho, Y. H. Shin, P. Moon, I. Yun

Research output: Contribution to journalArticle

### Abstract

As the metal-oxide-semiconductor field-effect transistor (MOSFET) technology has been developed, the short-channel effects become significant. To overcome these limitations, double gate (DG) MOSFET has been considered and predicting the device characteristics according to device parameters has been important. In this paper, we present the neural networks (NNET) modeling methodology to predict subthreshold characteristics such as threshold voltage (VTH) and subthreshold swing (SSUB) for DG MOSFET. After the NNET model is established, the genetic algorithm (GA) is used to find the device parameters' design space.

Original language English 1033-1037 5 ECS Transactions 60 1 https://doi.org/10.1149/06001.1033ecst Published - 2014

### Fingerprint

MOSFET devices
Genetic algorithms
Neural networks
Threshold voltage

### All Science Journal Classification (ASJC) codes

• Engineering(all)

### Cite this

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title = "Modeling of subthreshold characteristics for double gate MOSFET using neural networks and genetic algorithm",
abstract = "As the metal-oxide-semiconductor field-effect transistor (MOSFET) technology has been developed, the short-channel effects become significant. To overcome these limitations, double gate (DG) MOSFET has been considered and predicting the device characteristics according to device parameters has been important. In this paper, we present the neural networks (NNET) modeling methodology to predict subthreshold characteristics such as threshold voltage (VTH) and subthreshold swing (SSUB) for DG MOSFET. After the NNET model is established, the genetic algorithm (GA) is used to find the device parameters' design space.",
author = "Cho, {E. N.} and Shin, {Y. H.} and P. Moon and I. Yun",
year = "2014",
doi = "10.1149/06001.1033ecst",
language = "English",
volume = "60",
pages = "1033--1037",
journal = "ECS Transactions",
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Modeling of subthreshold characteristics for double gate MOSFET using neural networks and genetic algorithm. / Cho, E. N.; Shin, Y. H.; Moon, P.; Yun, I.

In: ECS Transactions, Vol. 60, No. 1, 2014, p. 1033-1037.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Modeling of subthreshold characteristics for double gate MOSFET using neural networks and genetic algorithm

AU - Cho, E. N.

AU - Shin, Y. H.

AU - Moon, P.

AU - Yun, I.

PY - 2014

Y1 - 2014

N2 - As the metal-oxide-semiconductor field-effect transistor (MOSFET) technology has been developed, the short-channel effects become significant. To overcome these limitations, double gate (DG) MOSFET has been considered and predicting the device characteristics according to device parameters has been important. In this paper, we present the neural networks (NNET) modeling methodology to predict subthreshold characteristics such as threshold voltage (VTH) and subthreshold swing (SSUB) for DG MOSFET. After the NNET model is established, the genetic algorithm (GA) is used to find the device parameters' design space.

AB - As the metal-oxide-semiconductor field-effect transistor (MOSFET) technology has been developed, the short-channel effects become significant. To overcome these limitations, double gate (DG) MOSFET has been considered and predicting the device characteristics according to device parameters has been important. In this paper, we present the neural networks (NNET) modeling methodology to predict subthreshold characteristics such as threshold voltage (VTH) and subthreshold swing (SSUB) for DG MOSFET. After the NNET model is established, the genetic algorithm (GA) is used to find the device parameters' design space.

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