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An advanced analytical neuro–space mapping technique with sensitivity analysis for transistor modeling
Author(s) -
Zhu Lin,
Liu Kaihua,
Liu Wenyuan,
Zhang Qijun,
Wu Haifeng,
Ma Yongtao,
Peng Bo
Publication year - 2016
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2206
Subject(s) - sensitivity (control systems) , computer science , space mapping , amplifier , signal (programming language) , transistor , artificial neural network , voltage , electronic engineering , artificial intelligence , algorithm , engineering , electrical engineering , computer network , bandwidth (computing) , programming language
Abstract This paper presents an advanced analytical neuro–space mapping (neuro‐SM) technique for accurate and efficient modeling of transistor devices. This is an improvement over the existing neuro‐SM, which aims to use neural networks to map a given approximate device model towards an accurate model. The proposed neuro‐SM retains the ability of the existing neuro‐SM in modifying the voltage relationship between the given approximate device model and the accurate model. The proposed technique can also map the current relationship between the given model and the accurate model. In this way, the proposed neuro‐SM can produce improved accuracy over the existing neuro‐SM. In addition, analytical formulas of mapping and sensitivities of the direct current, small‐signal S parameter, and large‐signal harmonic of the proposed neuro‐SM model with respect to mapping parameters and coarse‐model parameters are also derived. The sensitivity analysis can be used with a gradient‐based training technique to improve the model training efficiency. The validity and efficiency of the proposed approach are verified through 2 transistor modeling examples and use of the proposed neuro‐SM models in a large‐signal behavior analysis of an amplifier.