Model Auto Extraction for Gate-All-Around Silicon Nanowire MOSFETs Using A Decomposition-Based Many-Objective Evolutionary Algorithm

Ya Shu Yang, Yiming Li*

*Corresponding author for this work

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

Abstract

The merits of decomposition-based many-objective evolutionary algorithms are known in solving many-objective optimization problem (MaOP). Device model extraction is one of MaOPs, in this work, we first time apply a decomposition-based many-objective evolutionary algorithm with two types of adjustments for the direction vectors (MaOEA/D-2ADV) to model auto extraction of MOSFETs. This approach can fast extract 65 nm devices and sub-5-nm gate-all-around (GAA) silicon (Si) nanowire (NW) MOSFETs. The results of this study indicate that the automatic extraction by using the MaOEA/D-2ADV converges accurately, rapidly, and stably. It can not only jump out local traps but optimize final results efficiently for various devices. Compared with measured results, the accuracy of extracted ID-VG, ID-VD, gm, and gds are less than 1% within a few minutes.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Symposium on Quality Electronic Design, ISQED 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665494663
DOIs
StatePublished - 2022
Event23rd International Symposium on Quality Electronic Design, ISQED 2022 - Santa Jose, United States
Duration: 6 Apr 20227 Apr 2022

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
Volume2022-April
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Conference

Conference23rd International Symposium on Quality Electronic Design, ISQED 2022
Country/TerritoryUnited States
CitySanta Jose
Period6/04/227/04/22

Keywords

  • compact model parameter extraction
  • conventional planar devices
  • evolutionary algorithm
  • MaOEA/D-2ADV
  • Model auto extraction
  • MOSFETs
  • nanosheet
  • nanowire

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