Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning

• Autorzy: Ganum Adriana , Iskandar D. N. F. Awang , Chin Lim Phei , Fauzi Ahmad Hadinata

Identyfikatory

DOI

10.26636/jtit.2022.156321

• Języki publikacji: EN

Abstrakty

EN

Defect detection is an important step in industrial production of monocrystalline silicon. Through the study of deep learning, this work proposes a framework for classifying monocrystalline silicon wafer defects using deep transfer learning (DTL). An existing pre-trained deep learning model was used as the starting point for building a new model. We studied the use of DTL and the potential adaptation of Mo bileNetV2 that was pre-trained using ImageNet for extracting monocrystalline silicon wafer defect features. This has led to speeding up the training process and to improving performance of the DTL-MobileNetV2 model in detecting and classifying six types of monocrystalline silicon wafer defects (crack, double contrast, hole, microcrack, saw-mark and stain). The process of training the DTL-MobileNetV2 model was optimized by relying on the dense block layer and global average pooling (GAP) method which had accelerated the convergence rate and improved generalization of the classification network. The monocrystalline silicon wafer defect classification technique relying on the DTL-MobileNetV2 model achieved the accuracy rate of 98.99% when evaluated against the testing set. This shows that DTL is an effective way of detecting different types of defects in monocrystalline silicon wafers, thus being suitable for minimizing misclassification and maximizing the overall production capacities.

Słowa kluczowe

EN

automated optical inspection; machine learning; neural network; wafer imperfection identification

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2022
• Tom: nr 1
• Strony: 34--42
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Ganum Adriana

• LONGi (Kch) Sdn Bhd, 1072, Persiaran Elektronik 2, 93350 Kuching, Sarawak, Malaysia

autor

Iskandar D. N. F. Awang

• Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), 94300 Kota Samarahan, Sarawak, Malaysia

• https://orcid.org/0000-0002-0909-5670

autor

Chin Lim Phei

• Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), 94300 Kota Samarahan, Sarawak, Malaysia

• https://orcid.org/0000-0002-9987-0481

autor

Fauzi Ahmad Hadinata

• Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), 94300 Kota Samarahan, Sarawak, Malaysia

• https://orcid.org/0000-0003-0702-0074

Bibliografia

• [1] A. Ganum, „Improve wafer defects detection using deep learning approach", Master thesis, Universiti Malaysia Sarawak (UNIMAS), 2021.
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• [4] J. A. Mat Jizat, A. P. P. Abdul Majeed, A. F. Ab. Nasir, Z. Taha, and E. Yuen, „Evaluation of the machine learning classifier in wafer defects classification", ICT Express (in Press), 2021 (DOI: 10.1016/j.icte.2021.04.007).
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• [9] J.-H. Lee and J.-H. Lee, „A reliable defect detection method for patterned wafer image using convolutional neural networks with the transfer learning", in 4th Int. Conf. on Adv. Materials Res. and Manufacturing Technol. (AMRMT), Oxford, United Kingdom, vol. 647, 2019 (DOI: 10.1088/1757-899X/647/1/012010).
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• [12] C. Chen et al., „Deep learning on computational-resource-limited platforms: A survey", Mobile Information Systems, vol. 2020, Article ID 8454327, 19 pages, 2020 (DOI: 10.1155/2020/8454327).
• [13] J. Brownlee, „Estimate the number of experiment repeats for stochastic machine learning algorithms", 2020 [Online]. Available: https://machinelearningmastery.com/estimate-number-experiment-repeats-stochastic-machine-learning-algorithms/
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).