| Title: | OS26-7 Optimized Convolutional Neural Network Towards Effective Wafer Defects Classification |
|---|---|
| Publication: | ICAROB2024 |
| Volume: | 29 |
| Pages: | 865-870 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2024.OS26-7 |
| Author(s): | Koon Hian Ang, Koon Meng Ang, Chin Hong Wong, Abhishek Sharma, Chun Kit Ang, Kim Soon Chong, Sew Sun Tiang, Wei Hong Lim |
| Publication Date: | February 22, 2024 |
| Keywords: | Arithmetic optimization algorithm, Convolutional neural networks, Hyperparameter optimization, Wafer defect classification |
| Abstract: | Semiconductor defect inspection is crucial for yield improvement but is hindered by manual inspection's subjectivity and error. This paper employs Convolutional Neural Networks (CNNs) for automated wafer defect classification, addressing the challenges of time-intensive training and complex hyperparameter tuning. We propose the Arithmetic Optimization Algorithm (AOA) to efficiently optimize CNN hyperparameters like momentum, initial learning rate, maximum epochs, and L2 regularization. Our method reduces the trial-and-error in hyperparameter tuning. Using the AOA-optimized ResNet-18 model, our simulations show superior performance in defect classification compared to the unoptimized model, demonstrating its effectiveness and practical potential. |
| PDF File: | https://alife-robotics.co.jp/members2024/icarob/data/html/data/OS/OS26-7.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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