WaSSaBi: Wafer Selection With Self-Supervised Representations and Brain-Inspired Active Learning

Large datasets are often available for machine learning tasks. However, only very few contain labels for all the samples because labeling is a very labor-intensive process. Hence, large unlabeled datasets are available but inaccessible to traditional supervised learning methods. In this work, we combine two approaches to reduce the number of required labels. First, self-supervised learning (SSL) to utilize the large unlabeled dataset. SSL creates an encoder from those unlabeled samples that transforms the input into intermediate feature representations. Second, active learning is employed for the classification where labels are required. Active learning intelligently selects the most informative samples for manual labeling. Thus, it reduces the amount the labels required to achieve a high classification accuracy. The selected samples are used to train a brain-inspired hyperdimensional computing and random forest classifier. We demonstrate the outstanding performance of our approach with the example of wafer map defect pattern classification. It is a crucial diagnostic task helping to identify systematic problems in the manufacturing and improving yield. With our proposed method, a high 97% classification accuracy is achieved with only 6% of the labeled dataset for the first time. Our approach demonstrates the potential for training a machine learning model from less labeled samples by combining SSL with active learning.