Stochastic Feed-forward Attention Mechanism for Reliable Defect Classification and Interpretation

Jiyoon Lee, Chunghyup Mok, Sanghoon Kim, Seokho Moon, Seo Yeon Kim, Seoung Bum Kim

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


Defect analysis in manufacturing systems has been crucial for reducing product defect rates and improving process management efficiency. Recently, deep learning algorithms have been widely used to extract significant features from intertwined and complicated manufacturing systems. However, typical deep learning algorithms are black-box models in which the prediction process is difficult to understand. In this study, we propose a stochastic feed-forward attention network that consists of input feature level attention. The stochastic feed-forward attention network allows us to interpret the model by identifying the input features, dominant for prediction. In addition, the proposed model uses variational inferences to yield uncertainty information, which is a measure of the reliability of the interpretations. We conducted experiments in the field of display electrostatic chuck fabrication process to demonstrate the effectiveness and usefulness of our method. The results confirmed that our proposed method performs better and can reflect important input features.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2022 Intelligent Systems Conference IntelliSys Volume 1
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031160714
Publication statusPublished - 2023
EventIntelligent Systems Conference, IntelliSys 2022 - Virtual, Online
Duration: 2022 Sept 12022 Sept 2

Publication series

NameLecture Notes in Networks and Systems
Volume542 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceIntelligent Systems Conference, IntelliSys 2022
CityVirtual, Online

Bibliographical note

Funding Information:
Acknowledgments. This research was supported by the Brain Korea 21 FOUR and the IITP grant funded by the MSIT (No.2021–0-00034).

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Bayesian neural network
  • Defect prediction
  • Electrostatic chuck fabrication process
  • Explainable artificial intelligence
  • Feed-forward attention mechanism

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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