Stacked LSTM Based Wafer Classification

The sensors are used to analyze the quality of wafers in wafer manufacturing industries. The data from sensors is very helpful in framing solutions to predict the pass or fail status of the wafer by classifying high dimensional sensor data using machine learning techniques. In this work, we propose a stacked long short term memory (LSTM) approach i.e., a seq2seq architecture suitable for time-series data. We perform an exhaustive empirical analysis of the proposed model on the Seagate soft sensing dataset. The evaluation metric used is ROC-AUC score. The proposed stacked LSTM approach gave a best ROC-AUC score of 0.7445 on validation data and 0.729 on test data, which is significantly better than the baseline models.