Machine learning for circuit aging simulation

E. Rosenbaum, J. Xiong, A. Yang, Z. Chen, M. Raginsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The widespread availability of high-quality open source software for behavioral model optimization motivates the investigation of a behavioral approach to the modeling of aged circuits. A continuous-time formulation of a recurrent neural network (RNN) is compatible with transient circuit simulation, and this work evaluates RNN applicability to the modeling of aged circuits. For any reasonable input, the model should be required to produce an output response that is physically plausible. Approaches to imposing physical constraints on black-box models are outlined briefly.

Original languageEnglish (US)
Title of host publication2020 IEEE International Electron Devices Meeting, IEDM 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages39.1.1-39.1.4
ISBN (Electronic)9781728188881
DOIs
StatePublished - Dec 12 2020
Externally publishedYes
Event66th Annual IEEE International Electron Devices Meeting, IEDM 2020 - Virtual, San Francisco, United States
Duration: Dec 12 2020Dec 18 2020

Publication series

NameTechnical Digest - International Electron Devices Meeting, IEDM
Volume2020-December
ISSN (Print)0163-1918

Conference

Conference66th Annual IEEE International Electron Devices Meeting, IEDM 2020
Country/TerritoryUnited States
CityVirtual, San Francisco
Period12/12/2012/18/20

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering
  • Materials Chemistry

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