Bias-dependent small-signal modeling based on neuro-space mapping for MOSFET

Shoulin Li; Jiali Cheng; Bo Han; Jianjun Gao

doi:10.1002/mmce.20502

Bias-dependent small-signal modeling based on neuro-space mapping for MOSFET

Shoulin Li
, Jiali Cheng
, Bo Han
, Jianjun Gao^*

^*Corresponding author for this work

School of Physics and Electronic Science

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

In this article, bias-dependent small-signal modeling approach based on neuro-space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100 MHz-40 GHz confirming the validity and effectiveness of our approach. In addition, higher accuracy is achieved by our approach in contrast to conventional empirical model.

Original language	English
Pages (from-to)	182-189
Number of pages	8
Journal	International Journal of RF and Microwave Computer-Aided Engineering
Volume	21
Issue number	2
DOIs	https://doi.org/10.1002/mmce.20502
State	Published - Mar 2011

Keywords

MOSFET
neural network
parameter extraction
small-signal modeling
space mapping

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10.1002/mmce.20502

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title = "Bias-dependent small-signal modeling based on neuro-space mapping for MOSFET",

abstract = "In this article, bias-dependent small-signal modeling approach based on neuro-space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100 MHz-40 GHz confirming the validity and effectiveness of our approach. In addition, higher accuracy is achieved by our approach in contrast to conventional empirical model.",

keywords = "MOSFET, neural network, parameter extraction, small-signal modeling, space mapping",

author = "Shoulin Li and Jiali Cheng and Bo Han and Jianjun Gao",

year = "2011",

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AU - Li, Shoulin

AU - Cheng, Jiali

AU - Han, Bo

AU - Gao, Jianjun

PY - 2011/3

Y1 - 2011/3

N2 - In this article, bias-dependent small-signal modeling approach based on neuro-space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100 MHz-40 GHz confirming the validity and effectiveness of our approach. In addition, higher accuracy is achieved by our approach in contrast to conventional empirical model.

AB - In this article, bias-dependent small-signal modeling approach based on neuro-space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100 MHz-40 GHz confirming the validity and effectiveness of our approach. In addition, higher accuracy is achieved by our approach in contrast to conventional empirical model.

KW - MOSFET

KW - neural network

KW - parameter extraction

KW - small-signal modeling

KW - space mapping

UR - https://www.scopus.com/pages/publications/79952578680

U2 - 10.1002/mmce.20502

DO - 10.1002/mmce.20502

M3 - 文章

AN - SCOPUS:79952578680

SN - 1096-4290

VL - 21

SP - 182

EP - 189

JO - International Journal of RF and Microwave Computer-Aided Engineering

JF - International Journal of RF and Microwave Computer-Aided Engineering

IS - 2

ER -

Bias-dependent small-signal modeling based on neuro-space mapping for MOSFET

Abstract

Keywords

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