Enhanced Stacked Denoising Autoencoder-Based Feature Learning for Recognition of Wafer Map Defects

In semiconductor manufacturing systems, defects on wafer maps tend to cluster and then these spatial patterns provide important process information for helping operators in finding out root-causes of abnormal processes. Promptly recognizing wafer map defects is an effective way to increase manufactu...

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Bibliographic Details
Published in:IEEE transactions on semiconductor manufacturing Vol. 32; no. 4; pp. 613 - 624
Main Author: Yu, Jianbo
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Deep learning
defect recognition
Defects
Feature extraction
Feature recognition
Learning systems
Machine learning
manifold regularization
Manifolds
Manufacturing
Manufacturing processes
Noise reduction
Pattern recognition
Regularization
stacked denoising autoencoder
Wafer map
ISSN:0894-6507, 1558-2345
Online Access:Get full text
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Summary:In semiconductor manufacturing systems, defects on wafer maps tend to cluster and then these spatial patterns provide important process information for helping operators in finding out root-causes of abnormal processes. Promptly recognizing wafer map defects is an effective way to increase manufacturing process stability and then to improve yields. Deep learning has been widely applied and obtained many successes in image and visual analysis. This paper proposes an effective deep learning method, enhanced stacked denoising autoencoder (ESDAE) with manifold regularization for wafer map pattern recognition (WMPR) in manufacturing processes. This study will concentrate on developing a deep learning model to learn effective discriminative features from wafer maps through a deep network architecture for WMPR improvement. An indication based on ESDAE is developed for detecting map defects online. An ESDAE-based classifier is finally developed to implement recognition of wafer map defects. The most motivation for developing deep learning and manifold regularization techniques is to achieve higher accuracy and applicability than that of some regular recognizers. The effectiveness of the proposed method has been demonstrated by experimental results from a real-world wafer map dataset (WM-811K).
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ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2019.2940334