Digital Signal Processing and Artificial Intelligence for Automatic Learning

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Digital Signal Processing and Artificial Intelligence for Automatic Learning

A Novel Defect Classification Scheme based on Convolutional Autoencoder Skip Connection in Semiconductor Manufacturing

Jaegyeong Cha, Jongpil Jeong
Department of Smart Factory Convergence, Sungkyunkwan University South Korea., Department of Smart Factory Convergence, Sungkyunkwan University South Korea

Abstract: The semiconductor process cannot avoid defects due to its complex and diverse processes. In particular, wafer can be said to be the core of semiconductor manufacturing because they are directly related to the productivity of semiconductors. Therefore, detecting and classifying defects on wafers can help engineers address the root cause of defects and improve yield. In this paper, we propose a convolutional autoencoder using skip connection for wafer map defect classification. First, the encoder and decoder are designed by constructing a convolutional block. And connect the symmetrical blocks with skip connection. Finally, the training data of the classifier is encoded using the weights of the learned encoder. The loss of the model was successfully reduced with skip connection, and improved performance was obtained by reusing the encoder.

Keywords: Wafer, Convolutional Autoencoder, Skip Connection, Semiconductor A Novel Defect Classification Scheme based on Convolutional Autoencoder Skip Connection in Semiconductor Manufacturing

DOI:https://doi.org/10.6025/dspaial/2022/1/1/42-49

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References:

[1] Nakazawa., Takeshi., Kulkarni, Deepak V. (2018). Wafer map defect pattern classification and image retrieval using convolutional neural network. IEEE Transactions on Semiconductor Manufacturing, 31 (2) 309-314.
[2] Yuan-Fu., Yang. (2019). A deep learning model for identification of defect patterns in semiconductor wafer map. In 2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), 1-6.
[3] Yu, Jianbo. (2019). Enhanced stacked denoising autoencoder-based feature learning for recognition of wafer map defects. IEEE Transactions on Semiconductor Manufacturing 32 (4) 613-624.
[4] Shim., Jaewoong., Kang, Seokho., Cho, Sungzoon. (2020). Active learning of convolutional neural network for cost-effective wafer map pattern classification. IEEE Transactions on Semiconductor Manufacturing, 33 (2) 258-266.
[5] Bengio., Yoshua., Lamblin, Pascal., Popovici, Dan., Larochelle, Hugo. (2007). Greedy layer-wise training of deep networks. In: Advances in neural information processing systems, 153-160.
[6] He, Kaiming., Zhang, Xiangyu., Ren, Shaoqing., Sun, Jian. (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.
[7] Agarap, Abien Fred. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375.
[8] Allen, David M. (1971). Mean square error of prediction as a criterion for selecting variables.” Technometrics 13, no. 3, 469- 475.
[9] Amaral., Telmo., Kandaswamy, Chetak., Silva, Luís M., Alexandre, Luís A., Marques De Sa, Joaquim., Santos, Jorge M. (2014). Improving performance on problems with few labelled data by reusing stacked auto-encoders. In: 2014 13th International Conference on Machine Learning and Applications, 367-372.
[10] Liao., Bin., Xu, Jungang., Lv, Jintao., Zhou, Shilong. (2015). An image retrieval method for binary images based on DBN and softmax classifier. IETE Technical Review, 32 (4) 294-303.

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