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A self-supervised learning framework based on masked autoencoder for complex wafer bin map classification
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DOI：10.1016/j.eswa.2024.123601.png)
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Abstract
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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 selfsupervised 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 multilabel 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.
Keywords:
Self-supervised learning
Masked autoencoder
Complex wafer bin map
Automatic defect classification
Semiconductor manufacturing
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