Feature Extraction from Equipment Sensor Signals with Time Series Clustering and Its Application to Defect Prediction
| Autor: | Tomonari Masada, Takumi Eguchi, Daisuke Hamaguchi |
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| Rok vydání: | 2020 |
| Předmět: |
Series (mathematics)
Semiconductor device fabrication Computer science business.industry Feature extraction Semiconductor device modeling Pattern recognition 01 natural sciences Signal 010104 statistics & probability Feature (computer vision) Artificial intelligence 0101 mathematics Time series Cluster analysis business |
| Zdroj: | 2020 International Symposium on Semiconductor Manufacturing (ISSM). |
| Popis: | In semiconductor manufacturing processes, it is important to quickly identify any signs of the occurrence of defects. We applied a time-series clustering method to the signal data of processing equipment and obtained information related to the occurrence of defects. By using the information as the feature values of a prediction model, we were able to predict defects more accurately than by using only conventional feature values. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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