WaferHSL: Wafer failure pattern classification with efficient human-like staged learning

Qijing Wang, Martin D. F. Wong

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

As the demand for semiconductor products increases and the integrated circuits (IC) processes become more and more complex, wafer failure pattern classification is gaining more attention from manufacturers and researchers to improve yield. To further cope with the real-world scenario that there are only very limited labeled data and without any unlabeled data in the early manufacturing stage of new products, this work proposes an efficient human-like staged learning framework for wafer failure pattern classification named WaferHSL. Inspired by human s knowledge acquisition process, a mutually reinforcing task fusion scheme is designed for guiding the deep learning model to simultaneously establish the knowledge of spatial relationships, geometry properties and semantics. Furthermore, a progressive stage controller is deployed to partition and control the learning process, so as to enable humanlike progressive advancement in the model. Experimental results show that with only 10% labeled samples and no unlabeled samples, WaferHSL can achieve better results than previous SOTA methods trained with 60% labeled samples and a large number of unlabeled samples, while the improvement is even more significant when using the same size of labeled training set.

Original languageEnglish
Title of host publicationProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Electronic)9781450392174
DOIs
Publication statusPublished - 30 Oct 2022
Event41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022 - Virtual and, San Diego, United States
Duration: 30 Oct 20224 Nov 2022
https://2022.iccad.com/ (Conference website)
https://dl.acm.org/doi/proceedings/10.1145/3508352 (Conference proceedings)

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Conference

Conference41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
Country/TerritoryUnited States
CitySan Diego
Period30/10/224/11/22
Internet address

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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