利用雙層模型架構達成半導體製程參數設置最佳化

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姓名

楊泓益(HONG-YI YANG) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

利用雙層模型架構達成半導體製程參數設置最佳化
(The Study of Dual Discriminated Generative Adversarial Network)

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至系統瀏覽論文 (2029-7-1以後開放)

摘要(中)

近幾年來，工業4.0已經大幅改變了人們的生活，整合物聯網、大數據、人工智
慧、雲端運算，利用大量的資訊進行分析、預測以及應用達成更高效的生產技術。半
導體在其中扮演著十分關鍵的角色，半導體作為電子產品的核心，其製造流程對電子
產品影響很大。在半導體製造過程中，參數設定將極大地影響生產良率和能源效率。
目前的方法無法有效處理大量參數推薦以及參數之間的相互關係。在本文中，我們提
出一個能夠有效處理這些問題的模型-DDGAN。DDGAN使用雙層架構模型來推薦序列參
數，內層模型訓練目標為生成類似真實的資料，而外層模型的訓練目標則為生成良率
為正常的資料，藉由這種模型架構能夠有效的推薦出符合需求的參數序列。能夠使機
台的參數設置降低試錯成本並找出新的最佳參數組合。

摘要(英)

In recent years, Industry 4.0 has significantly transformed people′s lives by integrating
the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing. These
technologies leverage vast amounts of information to conduct analysis, make predictions, and
enhance the efficiency of production techniques. Semiconductors play a crucial role in this
context, serving as the core of electronic products, with their manufacturing processes greatly
influencing the performance of these products. During semiconductor manufacturing,
parameter settings profoundly impact production yield and energy efficiency. Current methods
are insufficient in effectively handling large-scale parameter recommendations and the
interrelationships among parameters. In this paper, we propose a model named DDGAN to
address these challenges effectively. DDGAN employs a dual-layer architecture model for
sequential parameter recommendation. The inner model is trained to generate data similar to
real-world data, while the Exterior model aims to generate data with normal yield rates. This
model architecture allows for the efficient recommendation of parameter sequences that meet
specific requirements, thereby reducing trial-and-error costs and identifying new optimal
parameter combinations for machine settings.

關鍵字(中)

★ 工業4.0
★ 生成對抗網路
★ 強化式學習
★ 深度學習
★ 參數最佳化

關鍵字(英)

★ Industry 4.0
★ Generative Adversarial Networks
★ Reinforcement Learning
★ Deep Learning
★ Parameter Optimization

論文目次

摘要 ........................................................................................................................................ i
Abstract ................................................................................................................................. ii
Table of Content ................................................................................................................... iii
Table of Figure...................................................................................................................... iv
Table of Table ........................................................................................................................ v
1.Introduction ........................................................................................................................ 1
2. Related work ..................................................................................................................... 7
2.1 Industrial Parameters and Process Optimization ................................................................................... 7
2.2 Model-Based Parameter and Process Optimization .............................................................................. 9
2.3 GANs for Optimization ..................................................................................................................................... 11
3. Model Proposal : DDGAN ............................................................................................... 13
3.1 Exterior GAN Architecture ............................................................................................................................. 15
3.2 Interior GAN Architecture ............................................................................................................................. 20
4. Experiment and Discussion ............................................................................................. 22
4.1 Baselines and Metrics ...................................................................................................................................... 24
4.2 Performance Comparison .............................................................................................................................. 26
4.3 Memory Influence Discussion ...................................................................................................................... 29
4.4 Discussion of Reward Parameter Setting ................................................................................................ 31
4.5 Performance Comparison of Different Weight Configurations for Predictor and Exterior
Discriminator .............................................................................................................................................................. 32
4.6 Ablation Study ..................................................................................................................................................... 35
4.7 Case Study ............................................................................................................................................................. 39
5. Conclusion ...................................................................................................................... 41
6. Reference ....................................................................................................................... 42

參考文獻

[1] Mota, P., Mariano, A., & Monteiro, S. (2018). TAXONOMY OF THE INDUSTRY 4.0: THEORETICAL AND PRACTICAL CONTRIBUTIONS TO A NEW CONTEXT. . https://doi.org/10.17605/OSF.IO/2Y9B4.
[2] Ahmed, Imran, Gwanggil Jeon and Francesco Piccialli. “From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where.” IEEE Transactions on Industrial Informatics 18 (2022): 5031-5042.
[3] S. Qin, J. Bernstein and C. Chan. "Hydrogen etching for semiconductor materials in plasma doping experiments." Journal of Electronic Materials, 25 (1996): 507-511. https://doi.org/10.1007/BF02666628.
[4] Qin, S., Bernstein, J., & Chan, C., 1996. Hydrogen etching for semiconductor materials in plasma doping experiments. Journal of Electronic Materials, 25, pp. 507-511. https://doi.org/10.1007/BF02666628.
[5] Ryu, S., Yim, S., Wi, S., Jung, S., Kim, S., & Kim, B., 2022. Design a GA-based PID Controller for a Pressure Control of a Process Chamber using a Time-domain System Identification. 2022 22nd International Conference on Control, Automation and Systems (ICCAS), pp. 484-487. https://doi.org/10.23919/ICCAS55662.2022.10003779.
[6] Huang, Z., Geyer, N., Werner, P., Boor, J., & Gösele, U., 2011. Metal‐Assisted Chemical Etching of Silicon: A Review. Advanced Materials, 23. https://doi.org/10.1002/adma.201001784.
[7] Bogumilowicz, Y., Hartmann, J., Truche, R., Campidelli, Y., Rolland, G., & Billon, T., 2005. Chemical vapour etching of Si, SiGe and Ge with HCl; applications to the formation of thin relaxed SiGe buffers and to the revelation of threading dislocations. Semiconductor Science and Technology, 20, pp. 127 - 134. https://doi.org/10.1088/0268-1242/20/2/004.
[8] Chen, L., & Chen, C. (2017). Bumping UBM metal residue defect analysis and improvement using techniques of design of experiment (DOE). 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 1751-1755.
[9] F. Briones, J. SANCHEZ LEAL and I. M. Pastrana. "Armentum: a hybrid direct search optimization methodology." Journal of Industrial Engineering International, 12 (2016): 407-417. https://doi.org/10.1007/S40092-016-0159-5.
[10] Erkan Isikli and S. Ugurlu. "The Role of Computational Intelligence in Experimental Design: A Literature Review." (2016): 213-235. https://doi.org/10.1007/978-3-319-24499-0_8.
[11] B. Duraković. "Design of Experiments Application, Concepts, Examples: State of the Art." Periodicals of Engineering and Natural Sciences (PEN), 5 (2017). https://doi.org/10.21533/PEN.V5I3.145.
[12] Uy, M., & Telford, J.K. (2009). Optimization by Design of Experiment techniques. 2009 IEEE Aerospace conference, 1-10.
[13] Gooding, O.W. (2004). Process optimization using combinatorial design principles: parallel synthesis and design of experiment methods. Current opinion in chemical biology, 8 3, 297-304 .
[14] Chen-Fu Chien, Kuo-Hao Chang and Wen-Chih Wang. "An empirical study of design-of-experiment data mining for yield-loss diagnosis for semiconductor manufacturing." Journal of Intelligent Manufacturing, 25 (2013): 961-972. https://doi.org/10.1007/s10845-013-0791-5.
[15] I. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio , “Generative adversarial nets.” In NIPS, pp.2672–2680, 2014.
[16] C. Canlin, T. Jian, X. Heng, W. Dandan and Y. Gang, "Virtual Sample Generation Method Based on GAN for Process Data with Its Application," 2022 34th Chinese Control and Decision Conference (CCDC), Hefei, China, 2022, pp. 242-247, doi: 10.1109/CCDC55256.2022.10034395.
[17] Yao, Z., & Zhao, C. (2022). FIGAN: A Missing Industrial Data Imputation Method Customized for Soft Sensor Application. IEEE Transactions on Automation Science and Engineering, 19, 3712-3722.
[18] Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu,”Seqgan: Sequence generative adversarial nets with policy gradient. “ In Thirty-first AAAI conference on artificial intelligence, ArXiv, abs/1609.05473, 2017.
[19] Park, Kijung, Gayeon Kim, Hee-Ra No, Hyun Woo Jeon , Gül E, Okudan Kremer, “Identification of Optimal Process Parameter Settings Based on Manufacturing Performance for Fused Filament Fabrication of CFR-PEEK.” Applied Sciences : n. pag, 2020.
[20] Nath, P., Olson, J.D., Mahadevan, S., & Lee, Y.T. (2020). Optimization of fused filament fabrication process parameters under uncertainty to maximize part geometry accuracy. Additive manufacturing, 35.
[21] Tang, H., Fan, Q., Xu, B., & Wen, J. (2004). A Technological Parameter Optimization Approach in Crude Oil Distillation Process Based on Neural Network. , 866-873. https://doi.org/10.1007/978-3-540-28648-6_138.
[22] Q. Fang, "A global optimization framework for parameter estimation of a wind generation unit model," IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, pp. 000048-000052, 2015.
[23] Jeang, A., & Li, H. (2009). An empirical study on the robust design of a semiconductor-bumping process. Journal of Materials Processing Technology, 209, 2986-2993.
[24] Wohlrabe, H., & Wolter, K. (2006). Practical Usage of Design of Experiments in the Production of SMT-Boards. 2006 1st Electronic System Integration Technology Conference, 2, 854-859. https://doi.org/10.1109/ESTC.2006.280111.
[25] Lin, James T., Chien‐Ming Chen, Chun-Chih Chiu , Hsin-Ying Fang,“Simulation optimization with PSO and OCBA for semiconductor back-end assembly.” Journal of Industrial and Production Engineering 30 : 452 - 460,2013.
[26] Xu, Haiqin, Linkun Li, Changqing Shi, Xiaoyu Li, Pengcheng Geng , Xiangsong Kong, “Parameter Optimization Method of Steam Generator Level Controller based on SVM and Improved PSO.” 2022 China Automation Congress (CAC) : 3209-3214,2022.
[27] Lin, J.T., Chen, C., Chiu, C., & Fang, H. (2013). Simulation optimization with PSO and OCBA for semiconductor back-end assembly. Journal of Industrial and Production Engineering, 30, 452 - 460.
[28] Chiu, C., Lai, C., & Chen, C. (2022). An evolutionary simulation-optimization approach for the problem of order allocation with flexible splitting rule in semiconductor assembly. Applied Intelligence, 53, 2593-2615.
[29] Chang, X., Dong, M., & Yang, D. (2013). Multi-objective real-time dispatching for integrated delivery in a Fab using GA based simulation optimization. Journal of Manufacturing Systems, 32, 741-751.
[30] P.J. García Nieto, E. García-Gonzalo, J.C. Álvarez Antón, V.M. González Suárez, R. Mayo Bayón, F. Mateos Martín,“A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance.”Journal of Computational and Applied Mathematics,Volume 330,Pages 877-895,2018.
[31] Zimmerling, Clemens, Christian Poppe, Oliver Stein , Luise Kärger, “Optimisation of Manufacturing Process Parameters for Variable Component Geometries using Reinforcement Learning.” Materials & Design ,2022.
[32] Clemens, Z., & Luise, K. (2023). Forming process optimisation for variable geometries by machine learning – Convergence analysis and assessment. Materials Research Proceedings.
[33] Guo, Jiaxian, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu , Jun Wang, “Long Text Generation via Adversarial Training with Leaked Information.” ArXiv abs/1709.08624 ,2017.
[34] Dey, R., Juefei-Xu, F., Boddeti, V.N., & Savvides, M., “RankGAN: A Maximum Margin Ranking GAN for Generating Faces..” ArXiv, abs/1812.08196 ,2018
[35] M. McCann, A. Johnston, “SECOM,” UCI Machine Learning Repository, 2008.
https://archive.ics.uci.edu/dataset/179/secom https://www.kaggle.com/datasets/paresh2047/uci-semcom/discussion
[36] Private company,” D- Electronics,”2024.
[37] Private company,” A-Optronics,”2023.
[38] Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W., & Woo, W. ” Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Neural Information Processing Systems.” 2015
[39] Private company,https://www.tel.com/product/manufacturing-process/index.html
[40] Fuśnik, Łukasz & Szafraniak, Bartlomiej & Paleczek, Anna & Grochala, Dominik & Rydosz, Artur. (2022). A Review of Gas Measurement Set-Ups. Sensors. 22. 2557. 10.3390/s22072557.

指導教授

陳以錚(YI-ZHENG CHEN)

審核日期

2024-7-18

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