Wafer Map Classifier using Deep Learning for Detecting Out-of-Distribution Failure Patterns
| Autor: | Yusung Kim, Donghee Cho, Jee-Hyong Lee |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Data processing Semiconductor device fabrication business.industry Computer science Deep learning Pattern recognition 02 engineering and technology Convolutional neural network Data modeling 020901 industrial engineering & automation Hardware_INTEGRATEDCIRCUITS Wafer Pattern matching Artificial intelligence business Classifier (UML) |
| Zdroj: | 2020 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA). |
| DOI: | 10.1109/ipfa49335.2020.9260877 |
| Popis: | Pattern analysis of wafer maps in semiconductor manufacturing is critical for failure analysis aspects or activities that increase yield. As deep learning becomes more popular than ever, research on the wafer map classification is active. However, more accurate pattern classification and data processing methods are required for the accuracy of commonality analysis to find suspected facilities using wafer map classification. It is difficult to represent all types of wafer maps in dozens of forms, and the frequency of wafer map shapes that vary with yield changes also requires the processing of undefined pattern wafer map data. We define out-of-distribution data of wafer map data that does not identify in the pattern classifier and suggest a network that uses the convolutional neural network (CNN) with residual units and training methods to classify it efficiently. We employ 15,436 real wafer map data for pattern classification and detection of out-of-distribution data. |
| Databáze: | OpenAIRE |
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