Neuro-Space Mapping for Modeling Heterojunction Bipolar Transistor

A neuro-space mapping （Neuro-SM） for modeling heterojunction bipolar transistor （HBT） is presented, which can automatically modify the input signals of the given model by neural network. The novel Neuro-SM formulations for DC and small-signal simulation are proposed to obtain the mapping network. Si...

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Bibliographic Details
Published inTransactions of Tianjin University Vol. 21; no. 1; pp. 90 - 94
Main Author 闫淑霞 成千福 邬海峰 张齐军
Format Journal Article
LanguageEnglish
Published Heidelberg Tianjin University 2015
School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
Subjects
Engineering
Humanities and Social Sciences
Mechanical Engineering
multidisciplinary
Science
信号仿真
建模
异质结双极晶体管
映射网络
神经网络
空间映射
自动修改
输入信号
optimization
neural network
neuro-space mapping
heterojunction bipolar transistor (HBT)
nonlinear device modeling
neuro-space map-ping
heterojunction bipolar transistor(HBT)
Online AccessGet full text
ISSN1006-4982
1995-8196
DOI10.1007/s12209-015-2493-x

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Summary:A neuro-space mapping （Neuro-SM） for modeling heterojunction bipolar transistor （HBT） is presented, which can automatically modify the input signals of the given model by neural network. The novel Neuro-SM formulations for DC and small-signal simulation are proposed to obtain the mapping network. Simulation results show that the errors between Neuro-SM models and the accurate data are less than 1%, demonstrating that the accurcy of the proposed method is higher than those of the existing models.
Bibliography:A neuro-space mapping （Neuro-SM） for modeling heterojunction bipolar transistor （HBT） is presented, which can automatically modify the input signals of the given model by neural network. The novel Neuro-SM formulations for DC and small-signal simulation are proposed to obtain the mapping network. Simulation results show that the errors between Neuro-SM models and the accurate data are less than 1%, demonstrating that the accurcy of the proposed method is higher than those of the existing models.
heterojunction bipolar transistor （HBT） ; nonlinear device modeling; neural network; neuro-space mapping; optimization
12-1248/T
Yan Shuxia , Cheng Qianfu, Wu Haifeng , Zhang Qijun （1. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China; 2. Department of Electronics, Carleton University, K1ShB6, Canada）
ISSN:1006-4982
1995-8196
DOI:10.1007/s12209-015-2493-x