Machine Learning Approach to Characteristic Fluctuation of Bulk FinFETs Induced by Random Interface Traps

Rajat Butola, Yiming Li*, Sekhar Reddy Kola

*Corresponding author for this work

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

3 Scopus citations

Abstract

Interface traps are of particular concern for highly scaled-down semiconductor devices. They cause trapping and de-trapping of charge carriers and have an adverse effect on device characteristics and variability. Therefore, in this work, the influence of randomly generated interface traps (RITs) on device characteristics of 16-nm-gate high-κ/metal gate bulk fin field-effect transistors (FinFETs) is investigated for experimentally validated simulated data. A machine learning (ML) model is proposed here to imitate the device simulation results. The impact of variation of these multi-point defects is analyzed by generating RITs at the interface of gate-oxide and silicon channel of the explored bulk FinFETs. The statistical fluctuations induced by RITs are analyzed by predicting the variations in threshold voltage (VTH), subthreshold slope (SS), drain-induced barrier lowering (DIBL), off-state current (IOFF), and transconductance (gm) using the proposed ML model with high accuracy and small error, in much less computational cost. This work shows the possibility of accelerating the random defects analysis using the technique of machine learning.

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

  • Bulk FinFET
  • characteristic fluctuation
  • interface trap
  • machine learning
  • random forest regressor

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