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 languageEnglish
Pages (from-to)1033-1037
Number of pages5
JournalECS Transactions
Volume60
Issue number1
DOIs
Publication statusPublished - 2014

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MOSFET devices
Genetic algorithms
Neural networks
Threshold voltage

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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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

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