Feature Extraction from Equipment Sensor Signals with Time Series Clustering and Its Application to Defect Prediction
Daisuke Hamaguchi, Tomonari Masada, Takumi Eguchi · 2020
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.