A self-supervised learning framework based on masked autoencoder for complex wafer bin map classification

Abstract

Wafer bin map (WBM) automatic classification is one of the critical challenges for semiconductor intelligent manufacturing. Many deep learning-based classification models have performed well in WBM classification, but all require a large amount of labeled data for training. Since real-world WBMs are highly complex and can be labeled correctly only by seasoned engineers, such requirements undermine the practical value of those methods. Several self-supervised learning methods have recently been proposed for WBM to improve classification performance. However, they still require much labeled data for fine-tuning and are only adapted for binary WBM with a single gross failure area. To address these limitations, this study introduces a self-supervised framework based on masked autoencoder (MAE) for complex WBMs with mixed bin signatures and multiple gross failure area patterns. A patchMC encoder is proposed to improve MAE’s representation ability for complex WBMs with mixed bin signatures. Moreover, the pre-trained MAE encoder with a multi-label classifier fine-tuned by labeled WBMs enables a few-shot classification of complex WBMs with multiple gross failure areas. Experimental validation of the proposed method is performed on a real-world complex WBM dataset from Intel Corporation. The results demonstrate that the proposed method can make good use of unlabeled WBMs and reduce the demand for labeled data to a few-shot level and, at the same time, guarantees a classification accuracy of more than 90%. By comparing MAE with other self-supervised learning methods, MAE outperforms other existing self-supervised methods for WBM data.