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.
|Title of host publication||Intelligent Systems and Applications - Proceedings of the 2022 Intelligent Systems Conference IntelliSys Volume 1|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||11|
|Publication status||Published - 2023|
|Event||Intelligent Systems Conference, IntelliSys 2022 - Virtual, Online|
Duration: 2022 Sept 1 → 2022 Sept 2
|Name||Lecture Notes in Networks and Systems|
|Conference||Intelligent Systems Conference, IntelliSys 2022|
|Period||22/9/1 → 22/9/2|
Bibliographical noteFunding Information:
Acknowledgments. This research was supported by the Brain Korea 21 FOUR and the IITP grant funded by the MSIT (No.2021–0-00034).
© 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