簡易檢索 / 詳目顯示

回結果列表
研究生： 方弗曼
Friyadi, Muhammad Firman
論文名稱： 人工智慧導向之微流體工程技術優化應用於高功率密度電子的熱控制
AI-Driven Microfluidic Engineering for Advanced Thermal Control in Power-Dense Electronics
指導教授： 游濟華
Yu, Chi-Hua
學位類別： 碩士
Master
系所名稱： 工學院 - 工程科學系
Department of Engineering Science
論文出版年： 2025
畢業學年度： 113
語文別： 英文
論文頁數： 98
中文關鍵詞： 微流體 、有限元素法 、代表性體積元素 、熱管理 、深度學習 、強化學習
外文關鍵詞： Microfluidic, Finite Element Method, Representative Volume Element, Thermal management, Deep Learning, Reinforcement Learning
相關次數： 點閱：41  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
    • ABSTRACT III 摘要 IV ACKNOWLEDGEMENT V TABLE OF CONTENTS VI LIST OF FIGURES VIII LIST OF TABLES XI 1 CHAPTER I INTRODUCTION 1 1.1 Background and Motivation 1 1.2 Research Objectives 3 1.3 Research Framework 4 2 CHAPTER II LITERATURE REVIEW 6 2.1 Microfluidic cooling design on A Chip Design 6 2.2 Finite Element Method 9 2.3 Representative Volume Element 11 2.4 Machine Learning 13 2.4.1 Convolution Neural Network 14 2.4.2 Reinforcement Learning 16 3 CHAPTER III RESEARCH METHODOLOGY 21 3.1 Microfluidic Cooling Design Overview 23 3.1.1 Overview of Microfluidic Cooling 23 3.1.2 Proposed Microfluidic Cooling Design Concept 24 3.2 Computational Model Development 28 3.2.1 Representative Volume Element Application 29 3.2.2 Design Variant Generation 31 3.3 FEM Simulation and Data Labeling 33 3.3.1 FEM Simulation Setup 34 3.3.2 Data Labeling and Preprocessing 39 3.4 Deep Learning for Property Prediction 46 3.4.1 Model Architecture 47 3.4.2 Data Inputs 49 3.5 Reinforcement Learning for Design Optimization 50 3.5.1 RL Environment Design 51 3.5.2 Deep Q Network (DQN Optimization) 54 4 CHAPTER IV RESULT AND DISCUSSION 59 4.1 Performance of Dual-Input CNN Model 60 4.1.1 Training Performance 60 4.1.2 Prediction Result 62 4.2 Reinforcement Learning Optimization Result 64 4.2.1 DQN Learning Performance 64 4.2.2 Optimize Design Result 65 4.3 Evaluation of Optimized Designs 67 4.3.1 Performance Test of Optimized Designs 68 4.3.2 Warpage and Package-Level Evaluation 75 5 CHAPTER V CONCLUSION AND FUTURE PROSPECTS 79 5.1 Conclusion 79 5.2 Future Prospects 80 REFERENCES LXXXII

    Agarap, A. F. (2017). An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification. http://arxiv.org/abs/1712.03541
    Bacarreza, O., Abe, D., Aliabadi, M. H., & Kopula Ragavan, N. (2012). Micromechanical Modeling of Advanced Composites. Journal of Multiscale Modelling, 04(02), 1250005. https://doi.org/10.1142/s1756973712500059
    Balasubramani, N. K., Zhang, B., Chowdhury, N. T., Mukkavilli, A., Suter, M., & Pearce, G. M. (2022). Micro-mechanical analysis on random RVE size and shape in multiscale finite element modelling of unidirectional FRP composites. Composite Structures, 282. https://doi.org/10.1016/j.compstruct.2021.115081
    Bunyan, S. T., Dhahad, H. A., Khudhur, D. S., & Yusaf, T. (2023). The Effect of Flow Field Design Parameters on the Performance of PEMFC: A Review. In Sustainability (Switzerland) (Vol. 15, Issue 13). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/su151310389
    Cai, Q., Gao, Z., An, J., Gao, S., & Grebogi, C. (2021). A Graph-Temporal Fused Dual-Input Convolutional Neural Network for Detecting Sleep Stages From EEG Signals. Ieee Transactions on Circuits & Systems Ii Express Briefs, 68(2), 777–781. https://doi.org/10.1109/tcsii.2020.3014514
    Carlescu, V., Prisacaru, G., & Olaru, N. D. (2014). FEM simulation on uniaxial tension of hyperelastic elastomers. Applied Mechanics and Materials, 659, 57–62. https://doi.org/10.4028/www.scientific.net/AMM.659.57
    Choi, J. W., Cho, Y. J., Lee, S., Lee, J. S., Lee, S. H., Choi, Y. H., Cheon, J., & Ha, J. Y. (2019). Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography. Investigative Radiology, 55(2), 101–110. https://doi.org/10.1097/rli.0000000000000615
    Cui, Y., Qin, Z., Wu, H., Li, M., & Hu, Y. (2021). Flexible Thermal Interface Based on Self-Assembled Boron Arsenide for High-Performance Thermal Management. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-21531-7
    Dhiman, T. K., Professor, A., & Kumar, T. K. S. (2023). Development and Optimization of Microscale Cooling Devices for Electronics Cooling. Nq. https://doi.org/10.48047/nq.2022.20.10.nq551260
    Giner, E., Sukumar, N., Tarancón, J. E., & Fuenmayor, F. J. (2009). An Abaqus implementation of the extended finite element method. Engineering Fracture Mechanics, 76(3), 347–368. https://doi.org/10.1016/J.ENGFRACMECH.2008.10.015
    Guo, J., Wang, D., Fan, R., Chen, W., & Zhao, G. (2016). Development of a material testing machine with multi-dimensional loading capability. Journal of Advanced Mechanical Design, Systems and Manufacturing, 10(2). https://doi.org/10.1299/jamdsm.2016jamdsm0017
    He, H., Peng, W., Liu, J., Chan, X. Y., Liu, S., Lu, L., & Ferrand, H. L. (2022). Microstructured BN Composites With Internally Designed High Thermal Conductivity Paths for 3D Electronic Packaging. Advanced Materials, 34(38). https://doi.org/10.1002/adma.202205120
    HPC. (2023, July). Extending the Viability of Air-cooling in High-Performance Data Centers With HPE Cray XD2000 Systems. https://www.hpcwire.com/2023/07/03/extending-the-viability-of-air-cooling-in-high-performance-data-centers-with-hpe-cray-xd2000-systems/#_ftn5
    Hsu, M. K., Chen, W., Huang, B. Y., Shen, L. H., Hsu, C. H., Chang, R. Y., & Yu, C. H. (2023). A deep learning empowered smart representative volume element method for long fiber woven composites. Frontiers in Materials, 10. https://doi.org/10.3389/fmats.2023.1179710
    JETCOOL Technologies Inc. (2024, March 30). How Power Density is Changing in Data Centers and What It Means for Liquid Cooling. https://jetcool.com/post/how-power-density-is-changing-in-data-centers/
    Jiao, G., Yin, J., Hua, Q., Du, B., Liu, X., & Gui, T.-L. (2008). Study of Thermo-Mechanical Stress Distribution for CBGA Package. 910–915. https://doi.org/10.1109/eptc.2008.4763546
    Kim, J. C., Ren, Z., Yuksel, A., Dede, E. M., Bandaru, P. R., Oh, D., & Lee, J. (2020). Recent Advances in Thermal Metamaterials and Their Future Applications for Electronics Packaging. Journal of Electronic Packaging, 143(1). https://doi.org/10.1115/1.4047414
    Kong, D., Kim, Y., Kang, M., Song, E., Hong, Y., Kim, H. S., Rah, K. J., Choi, H. G., Agonafer, D., & Lee, H. (2021). A holistic approach to thermal-hydraulic design of 3D manifold microchannel heat sinks for energy-efficient cooling. Case Studies in Thermal Engineering, 28. https://doi.org/10.1016/j.csite.2021.101583
    Krishnadasan, V. B., Suresh, P., & Balaji, C. (2024). Thermodynamic Analysis of Two-Fluid, Two-Phase Immersion Cooling System for Cooling of Electronic Components. Journal of Physics Conference Series, 2766(1), 012042. https://doi.org/10.1088/1742-6596/2766/1/012042
    Li, M., Li, S., Tian, Y., Fu, Y., Pei, Y., Zhu, W., & Ke, Y. (2023). A deep learning convolutional neural network and multi-layer perceptron hybrid fusion model for predicting the mechanical properties of carbon fiber. Materials and Design, 227. https://doi.org/10.1016/j.matdes.2023.111760
    Li, M., Li, S., Zhang, Z., Su, C., Wong, B., & Hu, Y. (2025). Advancing Thermal Management Technology for Power Semiconductors through Materials and Interface Engineering. Accounts of Materials Research, 6(5), 563–576. https://doi.org/10.1021/accountsmr.4c00349
    Lillicrap, T., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015). Continuous Control With Deep Reinforcement Learning. https://doi.org/10.48550/arxiv.1509.02971
    Liu, R., Nageotte, F., Zanne, P., de Mathelin, M., & Dresp-Langley, B. (2021). Deep reinforcement learning for the control of robotic manipulation: A focussed mini-review. Robotics, 10(1), 1–13. https://doi.org/10.3390/ROBOTICS10010022
    Marinković, B. A., White, M. A., Chen, J., & Sanson, A. (2022). Editorial: Recent Advances in Low-Positive, Zero, and Negative Thermal Expansion Materials: Fundamentals and Applications. Frontiers in Materials, 9. https://doi.org/10.3389/fmats.2022.873553
    Miao, S., Sui, J., Zhang, Y., Yao, F., & Liu, X. (2020). Experimental Study on Thermal Performance of a Bent Copper-Water Heat Pipe. International Journal of Aerospace Engineering, 2020, 1–10. https://doi.org/10.1155/2020/8632152
    Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236
    Morsali, S., Qian, D., & Minary-Jolandan, M. (2020). Designing bioinspired brick-and-mortar composites using machine learning and statistical learning. Communications Materials, 1(1). https://doi.org/10.1038/s43246-020-0012-7
    Niv, Y. (2009). Reinforcement learning in the brain. Journal of Mathematical Psychology, 53(3), 139–154. https://doi.org/10.1016/J.JMP.2008.12.005
    Okereke, M. I., & Akpoyomare, A. I. (2013). A virtual framework for prediction of full-field elastic response of unidirectional composites. Computational Materials Science, 70, 82–99. https://doi.org/10.1016/j.commatsci.2012.12.036
    Okereke, M., & Keates, S. (2018). Design of virtual domains. In Springer Tracts in Mechanical Engineering (Issue 9783319671246, pp. 145–164). Springer International Publishing. https://doi.org/10.1007/978-3-319-67125-3_5
    PANTOJA Zapico, R., Blanco, F., Felipe Verdeja, L., Pedro Sancho, J., Llavona, M. A., Zapico, R., Blanco, F., Verdeja, L. F., & Sancho, J. (1991). Methods for measuring thermal conductivity. https://www.researchgate.net/publication/265421213
    Papazafeiropoulos, G., Muñiz Calvente, M., & Martínez Pañeda, E. (2017). Abaqus2Matlab: A suitable tool for finite element post-processing. Advances in Engineering Software, 105, 9–16. https://doi.org/10.1016/j.advengsoft.2017.01.006
    Peng, J., Jury, E. C., Dönnes, P., & Ciurtin, C. (2021). Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges. Frontiers in Pharmacology, 12. https://doi.org/10.3389/fphar.2021.720694
    Pugliese, R., Regondi, S., & Marini, R. (2021). Machine learning-based approach: global trends, research directions, and regulatory standpoints. Data Science and Management, 4, 19–29. https://doi.org/10.1016/J.DSM.2021.12.002
    Ravichandiran, Sudharsan. (2018). Hands-on reinforcement learning with Python : master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow. Packt Publishing.
    Russell, S. J., Norvig, P., & Davis, E. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice-Hall.
    Shuford, J. (2024). Deep Reinforcement Learning Unleashing the Power of AI in Decision-Making. Journal of Artificial Intelligence General Science (JAIGS) ISSN:3006-4023, 1(1). https://doi.org/10.60087/JAIGS.V1I1.36
    Simeonnkeates, M. (n.d.). Springer Tracts in Mechanical Engineering Finite Element Applications A Practical Guide to the FEM Process. http://www.springer.
    Song, J., Huang, Y., Liu, Y., Ma, Z., Chen, L., Li, T., & Zhang, X. (2022). Numerical Investigation and Optimization of Cooling Flow Field Design for Proton Exchange Membrane Fuel Cell. Energies, 15(7). https://doi.org/10.3390/en15072609
    Sumit Saha. (2023, November 20). A Guide to Convolutional Neural Networks. https://saturncloud.io/blog/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way/
    Sutton, R. S. ., & Barto, A. G. . (2020). Reinforcement learning : an introduction. The MIT Press.
    Tchalla, A., Belouettar, S., Makradi, A., & Zahrouni, H. (2013). An ABAQUS toolbox for multiscale finite element computation. Composites Part B: Engineering, 52, 323–333. https://doi.org/10.1016/J.COMPOSITESB.2013.04.028
    Utomo, W. (2024). Implementation of a reinforcement learning system with deep q network algorithm in the amc dash mark i game. Journal of Soft Computing Exploration, 5(1), 18–25. https://doi.org/10.52465/JOSCEX.V5I1.271
    van Erp, R., Soleimanzadeh, R., Nela, L., Kampitsis, G., & Matioli, E. (2020). Co-designing electronics with microfluidics for more sustainable cooling. Nature, 585(7824), 211–216. https://doi.org/10.1038/s41586-020-2666-1
    Van Hirtum, A., Ahmad, M., Pelorson, X., & Hirtum, A. Van. (n.d.). Uni-axial stress-strain characterisation of silicone composite specimens derived from vocal folds replicas. European Journal of Mechanics-A/Solids, 2023. https://doi.org/10.1016/j.euromechsol.2023.105062ï
    Vilarrubí, M. (2024). Perspective Chapter: Smart Liquid Cooling Solutions for Advanced Microelectronic Systems. Heat Transfer - Advances in Fundamentals and Applications. https://doi.org/10.5772/INTECHOPEN.114147
    Wang, G. L., Yang, D. W., Wang, Y., Niu, D., Zhao, X. L., & Ding, G. F. (2015). Heat transfer and friction characteristics of the microfluidic heat sink with variously-shaped ribs for chip cooling. Sensors (Switzerland), 15(4), 9547–9562. https://doi.org/10.3390/s150409547
    Wang, M. L., Zheng, L. J., Bae, S., & Kang, H. W. (2023). Comprehensive performance enhancement of conformal cooling process using thermal-load-based topology optimization. Applied Thermal Engineering, 227. https://doi.org/10.1016/j.applthermaleng.2023.120332
    Wang, S., Yin, Y., Hu, C., & Rezai, P. (2018). 3D integrated circuit cooling with microfluidics. In Micromachines (Vol. 9, Issue 6). MDPI AG. https://doi.org/10.3390/mi9060287
    Wojtas, N., Hierold Micro, C., & Zurich, E. (2013). Microfluidic Heat Transfer Systems Optimized for Thermoelectric Heat Exchangers. Transducers & Eurosensors XXVII: The 17th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS & EUROSENSORS XXVII). https://doi.org/10.1109/Transducers.2013.6627032
    Wu, J., Zhou, E., An, H., Zhang, H., Hu, M., & Qin, G. (2022). Deep-Potential Enabled Multiscale Simulation of Gallium Nitride Devices on Boron Arsenide Cooling Substrates. https://doi.org/10.21203/rs.3.rs-1256813/v1
    Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9(4), 611–629. https://doi.org/10.1007/S13244-018-0639-9
    Yang, Z., Yu, C.-H., & Buehler, M. J. (2021). Deep learning model to predict complex stress and strain fields in hierarchical composites. Sci. Adv, 7(9). https://www.science.org
    Yoo, H. , K. S. , & L. J. (2022). Advances in Micro-channel Heat Sink Design: Challenges and Opportunities. International Journal of Thermal Sciences, 165.
    Zhao, D., Qian, X., Gu, X., Ayub Jajja, S., & Yang, R. (n.d.). Measurement Techniques for Thermal Conductivity and Interfacial Thermal Conductance of Bulk and Thin Film Materials.
    Zhou, L., & Yu, W. (2022). Improved Convolutional Neural Image Recognition Algorithm Based on LeNet-5. Journal of Computer Networks and Communications, 2022, 1–5. https://doi.org/10.1155/2022/1636203

    無法下載圖示 校內：2027-08-12公開
    校外：2027-08-12公開
    電子論文尚未授權公開，紙本請查館藏目錄
    QR CODE