Wafer Quality Inspection using Memristive LSTM, ANN, DNN and HTM

Kazybek Adam, Kamilya Smagulova, Olga Krestinskaya, Alex James Pappachen

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

Abstract

The automated wafer inspection and quality control is complex and time consuming task, which can be speed up using neuromorphic memristive architectures, as a separate inspection device or integrating directly into sensors. This paper presents the performance analysis and comparison of different neuromorphic architectures for patterned wafer quality inspection and classification. The application of non-volatile memristive devices in these architectures ensures low power consumption, small on-chip area scalability. We demonstrate that Long-Short Term Memory (LSTM) outperforms other architectures for the same number of training iterations, and has relatively low on-chip area and power consumption.

Original languageEnglish
Title of host publicationIEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538665923
DOIs
Publication statusPublished - Apr 2 2019
Event2018 IEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2018 - Chandigarh, India
Duration: Dec 16 2018Dec 18 2018

Publication series

NameIEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2018

Conference

Conference2018 IEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2018
CountryIndia
CityChandigarh
Period12/16/1812/18/18

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Wafer Quality Inspection using Memristive LSTM, ANN, DNN and HTM'. Together they form a unique fingerprint.

Cite this