Thermal placement optimization for embedded resistances based on orthogonal design and fuzzy genetic algorithm

2015 16th International Conference on Electronic Packaging Technology (ICEPT) Pub Date : 2015-09-03 DOI:10.1109/ICEPT.2015.7236656

Li Tianming, Zhang Ruibin, Huang Chunyue

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引用次数: 1

Abstract

Sheet resistance, resistor surface area, distance between the stack embedded resistor layers, distances between the surface of PCB and first layer of embedded resistor layers, distance between the stack embedded resistors in the same layer and current magnitude are selected as six key factors, which affect the temperature distribution. By using orthogonal array, the embedded resistor finite element analysis models which have different configuration parameters' levels combinations are designed. Simulation analysis of temperature field are carried out by using these models. The data of temperatures of stacked embedded resistors are analyzed variance analysis. With 90% of confidence, sheet resistance has the most significant effect on the temperature. Therefore, the research object is sheet resistance layout of resistance element in embedded substrate, which is optimizated by Fuzzy Genetic Algorithm The optimization result has more equal distribution of temperature, both of the highest temperature and the maximum temperature difference have a significant decrease. The effectiveness of the algorithm is verified by testing the temperature distribution of experimental samples through the infrared thermometer.

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基于正交设计和模糊遗传算法的嵌入式电阻热放置优化

选取影响温度分布的6个关键因素为片电阻、电阻表面积、堆叠内嵌电阻层之间的距离、PCB表面与第一层内嵌电阻层之间的距离、同一层内堆叠内嵌电阻层之间的距离和电流大小。采用正交阵列法，设计了具有不同配置参数电平组合的嵌入式电阻有限元分析模型。利用这些模型对温度场进行了仿真分析。采用方差分析方法对堆叠电阻的温度数据进行了分析。板材电阻对温度的影响最显著，有90%的置信度。因此，研究对象为嵌入式衬底中电阻元件的片状电阻布局，采用模糊遗传算法对其进行优化，优化结果温度分布更加均匀，最高温度和最大温差均有显著降低。通过红外测温仪测试实验样品的温度分布，验证了算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 16th International Conference on Electronic Packaging Technology (ICEPT)

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