Liquid metal microfluidic cooling system for high-efficiency thermal management via learning-based genetic algorithm
High heat flux density is a critical factor that limits the performance and reliability of miniaturized, high-power microelectronic systems. This study proposes a liquid metal (LM)-based microfluidic cooling system optimized through a data-driven computational framework based on an enhanced Genetic...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 161; s. 112324 |
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| Jazyk: | angličtina |
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Elsevier Ltd
12.12.2025
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| ISSN: | 0952-1976 |
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| Abstract | High heat flux density is a critical factor that limits the performance and reliability of miniaturized, high-power microelectronic systems. This study proposes a liquid metal (LM)-based microfluidic cooling system optimized through a data-driven computational framework based on an enhanced Genetic Algorithm (LC-GA), aiming to deliver an efficient thermal management solution for high-density integrated systems. By integrating LM near-junction cooling with microchannel heat dissipation in a silicon substrate, we developed a heterogeneous three-dimensional interconnect cooling architecture capable of optimizing thermal performance through algorithm-guided parameter tuning. To validate the proposed method, four distinct microchannel configurations were designed, fabricated, and experimentally tested. LM was introduced into the channels to conduct both experimental cooling tests and thermal performance simulations on a simulated heat source. The results demonstrate that this LM-based microfluidic cooling system, optimized through computational parameter determination, can effectively dissipate heat from chips with power consumption up to 800 W while maintaining stable thermal performance. Additionally, a response surface methodology combined with enhanced LC-GA was utilized for multi-factor sensitivity analysis and multi-objective optimization, enabling automatic determination of optimal design and operating parameters to balance thermal resistance and pressure drop. The optimized configuration reduced the maximum chip temperature to approximately 357.54 K, lowered the system pressure requirement, and improved the Performance Evaluation Criterion (PEC) to 2.327. This work provides a data-driven optimization approach that supports the development of high-performance integrated microsystems through algorithm-assisted thermal design. |
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| AbstractList | High heat flux density is a critical factor that limits the performance and reliability of miniaturized, high-power microelectronic systems. This study proposes a liquid metal (LM)-based microfluidic cooling system optimized through a data-driven computational framework based on an enhanced Genetic Algorithm (LC-GA), aiming to deliver an efficient thermal management solution for high-density integrated systems. By integrating LM near-junction cooling with microchannel heat dissipation in a silicon substrate, we developed a heterogeneous three-dimensional interconnect cooling architecture capable of optimizing thermal performance through algorithm-guided parameter tuning. To validate the proposed method, four distinct microchannel configurations were designed, fabricated, and experimentally tested. LM was introduced into the channels to conduct both experimental cooling tests and thermal performance simulations on a simulated heat source. The results demonstrate that this LM-based microfluidic cooling system, optimized through computational parameter determination, can effectively dissipate heat from chips with power consumption up to 800 W while maintaining stable thermal performance. Additionally, a response surface methodology combined with enhanced LC-GA was utilized for multi-factor sensitivity analysis and multi-objective optimization, enabling automatic determination of optimal design and operating parameters to balance thermal resistance and pressure drop. The optimized configuration reduced the maximum chip temperature to approximately 357.54 K, lowered the system pressure requirement, and improved the Performance Evaluation Criterion (PEC) to 2.327. This work provides a data-driven optimization approach that supports the development of high-performance integrated microsystems through algorithm-assisted thermal design. |
| ArticleNumber | 112324 |
| Author | Wang, Yucheng Chen, Kaiyu Wu, Yuwan Yu, Shenxin Bi, Antong Zhang, Wenyi Li, Zhiqiang Gao, Wanping Wang, Shaoxi |
| Author_xml | – sequence: 1 givenname: Yucheng orcidid: 0000-0001-5746-0081 surname: Wang fullname: Wang, Yucheng organization: School of Microelectronics, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China – sequence: 2 givenname: Antong surname: Bi fullname: Bi, Antong organization: School of Microelectronics, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China – sequence: 3 givenname: Kaiyu orcidid: 0000-0002-8305-9678 surname: Chen fullname: Chen, Kaiyu organization: School of Microelectronics, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China – sequence: 4 givenname: Shenxin surname: Yu fullname: Yu, Shenxin organization: School of Microelectronics, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China – sequence: 5 givenname: Wanping surname: Gao fullname: Gao, Wanping organization: School of Microelectronics, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China – sequence: 6 givenname: Wenyi surname: Zhang fullname: Zhang, Wenyi organization: School of Microelectronics, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China – sequence: 7 givenname: Yuwan surname: Wu fullname: Wu, Yuwan organization: School of Microelectronics, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China – sequence: 8 givenname: Zhiqiang surname: Li fullname: Li, Zhiqiang organization: School of Semiconductor and Physics, North University of China, Taiyuan, 030051, Shanxi, China – sequence: 9 givenname: Shaoxi orcidid: 0000-0002-0230-2059 surname: Wang fullname: Wang, Shaoxi email: shxwang@nwpu.edu.cn organization: School of Microelectronics, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China |
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| Cites_doi | 10.1016/j.enconman.2019.02.010 10.1063/1.4976566 10.1016/j.ijheatfluidflow.2025.109780 10.1002/anie.202400312 10.1016/j.energy.2024.131364 10.1016/j.applthermaleng.2024.125258 10.1515/cppm-2022-0084 10.1016/j.ijheatmasstransfer.2006.02.032 10.1016/j.icheatmasstransfer.2025.108706 10.1016/j.ijthermalsci.2020.106320 10.1007/s11664-013-2791-9 10.1016/j.energy.2024.133312 10.1016/j.energy.2024.134260 10.1063/1.1509101 10.1103/PhysRevE.98.063106 10.1016/j.ijthermalsci.2024.108992 10.3103/S1068798X24700977 10.1016/j.applthermaleng.2024.124024 10.1016/j.ijthermalsci.2025.109682 10.1109/EDL.1981.25367 10.1016/j.energy.2024.132846 10.1016/j.energy.2024.134145 10.3390/en18040807 10.1038/s41586-020-2666-1 10.3390/mi13010095 10.1016/j.ijthermalsci.2024.109426 10.1063/1.1606852 10.1002/adma.202313162 10.3390/mi14071418 10.1007/s10765-024-03471-8 |
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| Keywords | Thermal management Liquid metal Microfludic cooling Enhanced genetic algorithm Algorithm-guided parameter tuning |
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