Wafer bin map inspection based on DenseNet

Abstract

Wafer bin map (WBM) inspection is a critical approach for evaluating the semiconductor manufacturing process. An excellent inspection algorithm can improve the production efficiency and yield. This paper proposes a WBM defect pattern inspection strategy based on the DenseNet deep learning model, the structure and training loss function are improved according to the characteristics of the WBM. In addition, a constrained mean filtering algorithm is proposed to filter the noise grains. In model prediction, an entropy-based Monte Carlo dropout algorithm is employed to quantify the uncertainty of the model decision. The experimental results show that the recognition ability of the improved DenseNet is better than that of traditional algorithms in terms of typical WBM defect patterns. Analyzing the model uncertainty can not only effectively reduce the miss or false detection rate but also help to identify new patterns.

Keywords

wafer defect inspection / convolutional neural network / DenseNet / model uncertainty

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