A neural network-based manufacturing variability modeling of GaN HEMTs

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

3 Citations (Scopus)

Abstract

A new technique to accurately model the manufacturing variability of GaN HEMT using a neural network(NN) is presented in this paper. Compact model parameters are automatically generated through Principal component analysis (PCA) parameters from variations in I-V data. Together with the bias conditions, the compact model parameters are used to train a neural network. The NN-based compact model captures the I-V behavior of 115 GaN HEMT with excellent accuracy. The trained neural network is converted to a standard Verilog-A file that can be imported to a circuit simulator. The NN-based compact model is further evaluated in terms of complexity and simulation speed. The presented technique shows great potential in developing a fast, flexible, and accurate NN-based compact model that can be applied to any device technology.

Original languageEnglish
Title of host publication2024 IEEE 36th International Conference on Microelectronic Test Structures (ICMTS)
Subtitle of host publicationconference proceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9798350329896
ISBN (Print)9798350329902
DOIs
Publication statusPublished - 2024
Event36th IEEE International Conference on Microelectronic Test Structures, ICMTS 2024 - Edinburgh, United Kingdom
Duration: 15 Apr 202418 Apr 2024

Publication series

Name
ISSN (Print)1071-9032
ISSN (Electronic)2158-1029

Conference

Conference36th IEEE International Conference on Microelectronic Test Structures, ICMTS 2024
Country/TerritoryUnited Kingdom
CityEdinburgh
Period15/04/2418/04/24

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