Wafer defect pattern classification with detecting out-of-distribution

In semiconductor manufacturing, pattern analysis of wafer maps is important in terms of failure analysis and activities to increase yield. Image classification research using deep learning becomes popular; its application in wafer map classification in semiconductor manufacturing is also growing. However, to improve defect analysis accuracy, through-wafer map classification and clustering, more accurate pattern classification and data processing methods are required. It is difficult to identify the wafer map data expressed in the form of hundreds or thousands in dozens of patterns, and the frequency of the wafer map shape varies according to the change in yield. We present a learning method of a wafer map classifier that can process data of an undefined pattern without compromising the classifier's accuracy and evaluate its performance by applying the learning method to a model widely known in image classification. The data used in our study uses real wafer map data.