Application of Neural-symbol Model Based on Stacked Denoising Auto-encoders in Wafer Map Defect Recognition

被引：0
|
作者：
Liu G.-L. [1 ]
Yu J.-B. [1 ]
机构：
[1] School of Mechanical and Energy Engineering, Tongji University, Shanghai
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2022年 / 48卷 / 11期
基金：
中国国家自然科学基金;
关键词：
deep learning; knowledge discovery; stacked denoising auto-encoders; symbolic rule; Wafer map defect;
D O I：
10.16383/j.aas.c190857
中图分类号：
学科分类号：
摘要：
Deep neural network is a model with complex structure and multiple non-linear processing units. It has achieved great successes in wafer map pattern recognition through deep feature learning. In order to solve the problem of unexplained “black box” and excessive dependence on data in the applications of deep neural networks, this paper proposes a neural-symbol model based on a stacked denoising auto-encoders. Firstly, the symbolic rule system is designed according to the characteristics of stacked denoising auto-encoders. Secondly, according to the inner association between the network and the rules, a knowledge extraction and insertion algorithm is proposed to describe the deep network and improve the performance of the network. The experimental results on the industrial wafer map image set WM-811K show that the neural-symbol model based on stacked denoising auto-encoders not only achieves better defect pattern recognition performance, but also can effectively describe the internal logic of the neural network through rules, and its comprehensive performance is better than that of the current classical classification model. © 2022 Science Press. All rights reserved.
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收藏
页码：2688 / 2702
页数：14
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