G2LGAN: 對不平衡資料集進行資料擴增應用於晶圓圖缺陷分類

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：93

、訪客IP：3.137.218.83

姓名

王承暘(Chieng-Yang Wang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

G2LGAN: 對不平衡資料集進行資料擴增應用於晶圓圖缺陷分類
(G2LGAN: Data augmentation of imbalanced data sets for wafer map defect classification)

相關論文

★ 即時的SIFT特徵點擷取之低記憶體硬體設計	★ 即時的人臉偵測與人臉辨識之門禁系統
★ 具即時自動跟隨功能之自走車	★ 應用於多導程心電訊號之無損壓縮演算法與實現
★ 離線自定義語音語者喚醒詞系統與嵌入式開發實現	★ 晶圓圖缺陷分類與嵌入式系統實現
★ 語音密集連接卷積網路應用於小尺寸關鍵詞偵測	★ 補償無乘法數位濾波器有限精準度之演算法設計技巧
★ 可規劃式維特比解碼器之設計與實現	★ 以擴展基本角度CORDIC為基礎之低成本向量旋轉器矽智產設計
★ JPEG2000靜態影像編碼系統之分析與架構設計	★ 適用於通訊系統之低功率渦輪碼解碼器
★ 應用於多媒體通訊之平台式設計	★ 適用MPEG 編碼器之數位浮水印系統設計與實現
★ 適用於視訊錯誤隱藏之演算法開發及其資料重複使用考量	★ 一個低功率的MPEG Layer III 解碼器架構設計

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

半導體的製造需要經過多個複雜的化學程序，任一環節的錯誤皆會使生產的晶圓產生缺陷，因此晶圓缺陷分類是半導體產業維持並提升良率的關鍵任務。然而，目前的晶圓缺陷分類仍然由工程師透過人力進行分類，因此本論文透過神經網路在圖像分類上的優異表現來自動化分類晶圓圖缺陷。由於半導體製程的進步使得具有缺陷的晶圓圖難以收集。為了解決資料集不平衡與資料不足的問題，本論文提出了一個數據擴增方法與隨機欠採樣能夠有效的平衡資料集。在晶圓圖分類網路的部份本文使用輕量型的網路架構，除了能夠減少計算資源提高模型效率之外還能夠消除資料集所造成的過擬合 (Overfitting)的問題。
本論文提出的資料擴增是基於生成對抗網路 (Generative Adversarial Network, GAN)，所提出的方法 G2LGAN(Global to Local GAN)能夠優先學習資料集的全局特徵再學習個別類別的局部特徵，即使在資料集不平衡的情況下也能夠有效生成各個類別的資料以解決資料不足的問題。分類網路則是基於 MobileNetV2，根據實驗結果發現在晶圓缺陷分類任務上，與高維度的特徵相比，低維度的特徵較為重要，根據此結果來設計分類網路達到縮小模型並維持高準確度。
本文使用 WM-811K資料集進行驗證。由於該資料集具有嚴重的數據不平衡問題。本文集成了資料增強與隨機欠採樣的方法來優化資料集，並使用所提出的分類網路進行分類任務。實驗結果顯示，所提出的 G2LGAN能夠提升模型準確度約 9.15%。所提出的晶圓分類網路與現有的研究相比，在模型的參數量上節省了 58%，並且在準確度上有 1.39%與 F1-Score上有 12.35%的領先。

摘要(英)

Semiconductor manufacturing requires multiple complex chemical processes. Errors in any link will cause defects in the produced wafers. Therefore, wafer map defect classification is a key task for the semiconductor industry to maintain and improve yield. However, the current wafer defect classification is still performed by engineers through human labor. Therefore, this paper uses the excellent performance of neural networks in image classification to automatically classify wafer defects. Due to advances in semiconductor manufacturing processes, it is difficult to collect defective wafer maps. In order to solve the problem of data set imbalance and insufficient data, this paper proposes a data augmentation method and random undersampling that can effectively balance the data set. In the part of the wafer map classification network, this article uses a lightweight network architecture, which not only reduces computing resources and improves model efficiency, but also eliminates the problem of overfitting caused by data sets.
The data augmentation proposed in this paper is based on the Generative Adversarial Network (GAN). The proposed method G2LGAN (Global to Local GAN) can first learn the global features of the data set and then learn the local features of individual classes, even in In the case of imbalanced data sets, various classes of data can be effectively generated to solve the problem of insufficient data. The classification network is based on MobileNetV2. According to the experimental results, it is found that in wafer defect classification tasks, low-dimensional features are more important than high-dimensional features. Based on this result, the classification network is designed to reduce the model and maintain high accuracy.
This article uses the WM-811K data set for verification. Because this data set has serious data imbalance problem. This paper integrates data enhancement and random undersampling methods to optimize the data set, and uses the proposed classification network for classification tasks. Experimental results show that the proposed G2LGAN can improve the accuracy of the model by approximately 9.15%. Compared with the existing research, the proposed wafer classification network saves 58% in the amount of model parameters, and has a accuracy of 1.39% and a 12.35% lead in F1-Score.

關鍵字(中)

★ 生成對抗網路
★ 晶圓缺線分類
★ 不平衡資料集

關鍵字(英)

★ GAN
★ Wafer map defect classification
★ imbalanced data

論文目次

摘要.................... I
Abstract............... II
致謝.................. III
目錄 .................. IV
表目錄.................. V
圖目錄 ................ VI
一、序論................ 1
1-1研究背景與動機 ........1
1-2論文架構 ............ 5
二、文獻探討 ............ 6
2-1資料增強 ............ 6
2-2晶圓圖缺陷分類 ...... 10
三、網路模型設計與實驗 .. 14
3-1資料集 .............. 14
3-2資料前處理 .......... 17
3-3 G2LGAN網路架構 ..... 17
3-3晶圓圖缺陷分類網路 ... 22
四、結果與討論 .......... 25
4-1數據集和評估指標 .......25
4-2實施細節 ............. 30
4-3 G2LGAN實驗結果 ...... 32
4-4 晶圓圖缺陷分類實驗結果 34
五、結論 ................ 40
參考文獻 ................ 41

參考文獻

[1] R. Uzsoy, C. Y. Lee, L. A. Martin-Vega, “A review of production planning and scheduling models in the semiconductor industry part I: System characteristics, performance evaluation and production planning,” IIE Trans. 1992, 24, 47–60.
[2] E. Akçali, K. Nemoto, and R. Uzsoy, “Cycle-time improvements for photolithography process in semiconductor manufacturing,” IEEE Trans. Semicond. Manuf., vol. 14, no. 1, pp. 48–56, 2001.
[3] E. Akçali, K. Nemoto, and R. Uzsoy, “Cycle-time improvements for photolithography process in semiconductor manufacturing,” IEEE Trans. Semicond. Manuf., vol. 14, no. 1, pp. 48–56, 2001.
[4] A. Carlson, T. Le “Correlation of Wafer Backside Defects to Photolithography Hot Spots Using Advanced Macro Inspection” 2006 31st International Symposium, Microlithography - An SPIE Event.
[5] M. H. Hansen, V. N. Nair, and D. J. Friedman, “Monitoring wafer map data from integrated circuit fabrication processes for spatially clustered defects,” Technometrics, vol. 39, no. 3, 1997, in press.
[6] K. Kyeong and H. Kim, “Classification of mixed-type defect patterns in wafer bin maps using convolutional neural networks,” IEEE Trans. Semicond. Manuf., vol. 31, no. 3, pp. 395–402, Aug. 2018.
[7] J. Wang, Y. Yang, J. Mao, Z. Huang, C. Huang, and W. Xu, “Cnn-rnn: A unified framework for multi-label image classification,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016, pp. 2285–2294.
[8] H. Lee and H. Kwon, “Going deeper with contextual CNN for hyperspectral image classification,” IEEE Trans. Image Process., vol. 26, no. 10, pp. 4843–4855, Oct. 2017.
[9] M. Zhang, W. Li, and Q. Du, “Diverse region-based CNN for hyperspectral image classification,” IEEE Trans. Image Process., vol. 27, no. 6, pp. 2623–2634, Jun. 2018.
[10] D. Kim, S.H Huh, S.J. Seo and G.T. Park, “A Novel Soft Computing Technique for the Shortcoming of the Polynomial Neural Network,” Int J. Control, Autom., & Syst., vol. 2, pp. 189-200, 2004
[11] J. Li, J.-H. Cheng, J.-Y. Shi, and F. Huang, “Brief introduction of back propagation (BP) neural network algorithm and its improvement,” in Advances in Computer Science and Information Engineering, Heidelberg, Germany: Springer, 2012, pp. 553–558
[12] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al. Generative adversarial nets. In Advances in Neural Information Processing Systems, pages 2672–2680, 2014.
[13] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2018, pp. 4510–4520.
[14] C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J. Big Data, vol. 6, no. 1, p. 60, Dec. 2019.
[15] D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proc. Int. Conf. Learning Representations, 2014. 42
[16] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired imageto-image translation using cycle-consistent adversarial networks,” in ICCV, 2017.
[17] Y. Shen, J. Gu, X. Tang, and B. Zhou, “Interpreting the latent space of GANs for semantic face editing,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2020, pp. 9243–9252.
[18] K. Nazeri, E. Ng, and M. Ebrahimi, “Image colorization using generative adversarial networks,” in Int’l Conf. on Articulated Motion and Deformable Objects, 2018.
[19] C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, “Photo-realistic single image super-resolution using a generative adversarial network,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2017, pp. 1–10.
[20] M. Binkowski, J. Donahue, S. Dieleman, A. Clark, ´ E. Elsen, N. Casagrande, L. C. Cobo, and K. Simonyan, “High fidelity speech synthesis with adversarial networks,” in International Conference on Learning Representations (ICLR), 2020.
[21] M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification,” Neurocomputing, vol. 321, pp. 321–331, Dec. 2018.
[22] MIRZA, Mehdi; OSINDERO, Simon. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.
[23] ODENA, Augustus; OLAH, Christopher; SHLENS, Jonathon. Conditional image synthesis with auxiliary classifier gans. In: International conference on machine learning. PMLR, 2017. p. 2642-2651.
[24] G. Mariani, F. Scheidegger, R. Istrate, C. Bekas, and C. Malossi. BAGAN: Data augmentation with balancing GAN. arXiv preprint arXiv:1803.09655, 2018.
[25] M.-J. Wu, J.-S. R. Jang, and J.-L. Chen, “Wafer map failure pattern recognition and similarity ranking for large-scale data sets,” IEEE Trans. Semicond. Manuf., vol. 28, no. 1, pp. 1–12, Feb. 2015.
[26] T. Ishida, I. Nitta, D. Fukuda, and Y. Kanazawa, “Deep learningbased wafer-map failure pattern recognition framework,” in Proc. 20th Int. Symp. Qual. Electron. Design (ISQED), Santa Clara, CA, USA, Mar. 2019, pp. 291–297
[27] U. Batool, M. I. Shapiai et al., “Convolutional Neural Network for Imbalanced Data Classification of Silicon Wafer Defects,” in Proceedings of 2020 16th IEEE International Colloquium on Signal Processing Its Applications (CSPA), February 2020, pp. 230–235.
[28] M. B. Alawieh, D. Boning, and D. Z. Pan, ‘‘Wafer map defect patterns classification using deep selective learning,’’ in Proc. 57th ACM/IEEE Design Autom. Conf. (DAC), Jul. 2020, pp. 1–6.
[29] T.-H. Tsai and Y.-C. Lee, “A Light-Weight Neural Network for Wafer Map Classification Based on Data Augmentation,” IEEE Transactions on Semiconductor Manufacturing, pp. 1 – 1, July 2020.
[30] A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015. 43
[31] Odena, et al., "Deconvolution and Checkerboard Artifacts", Distill, 2016. http://doi.org/10.23915/distill.00003.
[32] M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” in Proc. 34th Int. Conf. Machine Learning, 2017, pp. 214–223.
[33] L. Sun, J. Chen and K. Xie, ”Deep and shallow features fusion based on deep convolutional neural network for speech emotion recognition,” International Journal of Speech Technology, vol. 21, no. 4, pp. 931-940, December. 2018.
[34] Q. Xu, G. Huang, Y. Yuan, C. Guo, Y. Sun, F. Wu, and K. Weinberger, “An empirical study on evaluation metrics of generative adversarial networks,” arXiv preprint arXiv:1806.07755, 2018.

指導教授

蔡宗漢(Tsung-Han Tsai)

審核日期

2022-1-12

推文