Automatic device model parameter extractions via hybrid intelligent methodology

Cheng Che Liu, Yiming Li*, Ya Shu Yang, Chieh Yang Chen, Min Hui Chuang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We report an advanced hybrid intelligent methodology for device model parameter extractions combining multiobjective evolutionary algorithms, numerical optimization methods, and unsupervised learning neural networks on a unified optimization framework. The results between experimentally measured data and the calculation from industrial standard compact models are accurate, stable and convergent rapidly for all I-V curves. Verifications from diodes, bipolar transistors, MOSFETs, FinFETs, to nanowire MOSFETs confirm the robustness of the developed prototype, where the extraction is within 5% of accuracy.

Original languageEnglish
Title of host publication2020 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages355-358
Number of pages4
ISBN (Electronic)9784863487635
DOIs
StatePublished - 23 Sep 2020
Event2020 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2020 - Virtual, Kobe, Japan
Duration: 3 Sep 20206 Oct 2020

Publication series

NameInternational Conference on Simulation of Semiconductor Processes and Devices, SISPAD
Volume2020-September

Conference

Conference2020 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2020
CountryJapan
CityVirtual, Kobe
Period3/09/206/10/20

Keywords

  • Automatic model parameter extraction
  • Hybrid intelligent methodology
  • Multiobjective evolutionary algorithms.

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