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

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

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

3 Scopus citations


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 (US)
Title of host publicationIEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538665923
StatePublished - Jul 2 2018
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23


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