In semiconductor manufacturing, detecting defect patterns is important because they are directly related to the root causes of failures in the wafer process. The rapid advancement of the integrated circuit technology has recently led to more frequent occurrences of mixed-type defect patterns, wherein two or more defect patterns simultaneously occur in a wafer bin map. The detection of these mixed patterns is more difficult than that of single patterns. To detect these mixed patterns, binary relevance approaches based on convolutional neural networks have been proposed. However, as the manufacturing process has been advanced and integrated, various failure types are newly detected, thus the number of single models can be continuously increased following the diversification of defect types. Therefore, we propose an effective framework for detecting mixed-type patterns in which a simple single model, called the single shot detector, is employed. By applying the proposed model to the WM-811K dataset, we show that our framework outperforms existing CNN-based methods and also provides defect location information.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (2020R1A2C2005026).
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
- Artificial Intelligence