Unsupervised Learning for Wafer Surface Defect Pattern Recognition

Abstract Wafer manufacturing is an important step in quality control and analysis in the semiconductor industry. The defect pattern classification algorithm of wafer maps has received extensive attention from academia and industry. At present, most methods for detecting wafer surface defect patterns focus on static data model classification and analysis. However, in the production process, static data models cannot satisfy the dynamic analysis of wafer defect patterns in the form of streaming data. In this regard, this paper proposes a wafer surface defect pattern detection method based on incremental learning. Our experiment uses Resnet as the backbone network, and the data set uses the WM811K wafer data set. Experiments have proved that our method can achieve better classification accuracy in the field of wafer defect detection, which provides the possibility for continuous learning of wafer defects in the future.

Download Full-text

Defect Pattern Recognition of Transformers Based on the method of vectorization of parameters

2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE) ◽

10.1109/ichve49031.2020.9280029 ◽

2020 ◽

Author(s):

Junwei Diao ◽

Yao Yuan ◽

Ruihai Li ◽

Jiahui Yang ◽

Lianwei Bao

Keyword(s):

Pattern Recognition ◽

Defect Pattern

Download Full-text

Fast unsupervised learning for visual pattern recognition using spike timing dependent plasticity

Neurocomputing ◽

10.1016/j.neucom.2017.04.003 ◽

2017 ◽

Vol 249 ◽

pp. 212-224 ◽

Cited By ~ 16

Author(s):

Daqi Liu ◽

Shigang Yue

Keyword(s):

Pattern Recognition ◽

Unsupervised Learning ◽

Spike Timing ◽

Visual Pattern ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity ◽

Visual Pattern Recognition

Download Full-text

Aspects of Intelligence in an "SP" Database System

Database Technologies ◽

10.4018/978-1-60566-058-5.ch043 ◽

2009 ◽

pp. 725-754

Author(s):

J. Gerard Wolff

Keyword(s):

Pattern Recognition ◽

Information Retrieval ◽

Natural Language ◽

Unsupervised Learning ◽

Probabilistic Reasoning ◽

Database System ◽

Theory Of Computing ◽

New Knowledge ◽

Database Models

This chapter describes some of the kinds of “intelligence” that may be exhibited by an intelligent database system based on the SP theory of computing and cognition. The chapter complements an earlier paper on the SP theory as the basis for an intelligent database system (Wolff, forthcoming b) but it does not depend on a reading of that earlier paper. The chapter introduces the SP theory and its main attractions as the basis for an intelligent database system: that it uses a simple but versatile format for diverse kinds of knowledge, that it integrates and simplifies a range of AI functions, and that it supports established database models when that is required. Then with examples and discussion, the chapter illustrates aspects of “intelligence” in the system: pattern recognition and information retrieval, several forms of probabilistic reasoning, the analysis and production of natural language, and the unsupervised learning of new knowledge.

Download Full-text