Thermodynamic Multi-Field Coupling Optimization of Microsystem Based on Artificial Intelligence
An efficient multi-objective optimization method of temperature and stress for a microsystem based on particle swarm optimization (PSO) was established, which is used to map the relationship between through-silicon via (TSV) structural design parameters and performance objectives in the microsystem,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/14/2/411 |
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| author | Guangbao Shan Xudong Wu Guoliang Li Chaoyang Xing Shengchang Zhang Yu Fu |
| author_facet | Guangbao Shan Xudong Wu Guoliang Li Chaoyang Xing Shengchang Zhang Yu Fu |
| author_sort | Guangbao Shan |
| collection | DOAJ |
| description | An efficient multi-objective optimization method of temperature and stress for a microsystem based on particle swarm optimization (PSO) was established, which is used to map the relationship between through-silicon via (TSV) structural design parameters and performance objectives in the microsystem, and complete optimization temperature, stress and thermal expansion deformation efficiently. The relationship between the design and performance parameters is obtained by a finite element method (FEM) simulation model. The neural network is built and trained in order to understand the mapping relationship. Then, the design parameters are iteratively optimized using the PSO algorithm, and the FEM results are used to verify the efficiency and reliability of the optimization methods. When the optimization target of peak temperature, bump temperature, TSV temperature, maximum stress and maximum thermal deformation are set as 100 °C, 55 °C, 35 °C, 180 Mpa and 12 μm, the optimization results are as follows: the peak temperature is 97.90 °C, the bump temperature is 56.01 °C, the TSV temperature is 31.52 °C, the maximum stress is 247.4 Mpa and the maximum expansion deformation is 11.14 μm. The corresponding TSV structure design parameters are as follows: the radius of TSV is 10.28 μm, the pitch is 65 μm and the thickness of SiO<sub>2</sub> is 0.83 μm. The error between the optimization result and the target temperature is 2.1%, 1.8%, 9.9%, 37.4% and 7.2% respectively. The PSO method has been verified by regression analysis, and the difference between the temperature and deformation optimization results of the FEM method is not more than 3%. The stress error has been analyzed, and the reliability of the developed method has been verified. While ensuring the accuracy of the results, the proposed optimization method reduces the time consumption of a single simulation from 2 h to 70 s, saves a lot of time and human resources, greatly improves the efficiency of the optimization design of microsystems, and has great significance for the development of microsystems. |
| first_indexed | 2024-03-11T08:24:42Z |
| format | Article |
| id | doaj.art-e4cfa9c1fd934a9099b7fb239cf7e227 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-11T08:24:42Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-e4cfa9c1fd934a9099b7fb239cf7e2272023-11-16T22:11:53ZengMDPI AGMicromachines2072-666X2023-02-0114241110.3390/mi14020411Thermodynamic Multi-Field Coupling Optimization of Microsystem Based on Artificial IntelligenceGuangbao Shan0Xudong Wu1Guoliang Li2Chaoyang Xing3Shengchang Zhang4Yu Fu5School of Microelectronics, Xidian University, Xi’an 710071, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaBeijing Institute of Aerospace Control Devices, Beijing 100039, ChinaSchool of Microelectronics, Xidian University, Xi’an 710071, ChinaChina Academy of Aerospace Standardization and Product Assurance, Beijing 100071, ChinaAn efficient multi-objective optimization method of temperature and stress for a microsystem based on particle swarm optimization (PSO) was established, which is used to map the relationship between through-silicon via (TSV) structural design parameters and performance objectives in the microsystem, and complete optimization temperature, stress and thermal expansion deformation efficiently. The relationship between the design and performance parameters is obtained by a finite element method (FEM) simulation model. The neural network is built and trained in order to understand the mapping relationship. Then, the design parameters are iteratively optimized using the PSO algorithm, and the FEM results are used to verify the efficiency and reliability of the optimization methods. When the optimization target of peak temperature, bump temperature, TSV temperature, maximum stress and maximum thermal deformation are set as 100 °C, 55 °C, 35 °C, 180 Mpa and 12 μm, the optimization results are as follows: the peak temperature is 97.90 °C, the bump temperature is 56.01 °C, the TSV temperature is 31.52 °C, the maximum stress is 247.4 Mpa and the maximum expansion deformation is 11.14 μm. The corresponding TSV structure design parameters are as follows: the radius of TSV is 10.28 μm, the pitch is 65 μm and the thickness of SiO<sub>2</sub> is 0.83 μm. The error between the optimization result and the target temperature is 2.1%, 1.8%, 9.9%, 37.4% and 7.2% respectively. The PSO method has been verified by regression analysis, and the difference between the temperature and deformation optimization results of the FEM method is not more than 3%. The stress error has been analyzed, and the reliability of the developed method has been verified. While ensuring the accuracy of the results, the proposed optimization method reduces the time consumption of a single simulation from 2 h to 70 s, saves a lot of time and human resources, greatly improves the efficiency of the optimization design of microsystems, and has great significance for the development of microsystems.https://www.mdpi.com/2072-666X/14/2/411TSVoptimizationparticle swarm optimization algorithmmicrosystem |
| spellingShingle | Guangbao Shan Xudong Wu Guoliang Li Chaoyang Xing Shengchang Zhang Yu Fu Thermodynamic Multi-Field Coupling Optimization of Microsystem Based on Artificial Intelligence Micromachines TSV optimization particle swarm optimization algorithm microsystem |
| title | Thermodynamic Multi-Field Coupling Optimization of Microsystem Based on Artificial Intelligence |
| title_full | Thermodynamic Multi-Field Coupling Optimization of Microsystem Based on Artificial Intelligence |
| title_fullStr | Thermodynamic Multi-Field Coupling Optimization of Microsystem Based on Artificial Intelligence |
| title_full_unstemmed | Thermodynamic Multi-Field Coupling Optimization of Microsystem Based on Artificial Intelligence |
| title_short | Thermodynamic Multi-Field Coupling Optimization of Microsystem Based on Artificial Intelligence |
| title_sort | thermodynamic multi field coupling optimization of microsystem based on artificial intelligence |
| topic | TSV optimization particle swarm optimization algorithm microsystem |
| url | https://www.mdpi.com/2072-666X/14/2/411 |
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