基于SVR与改进NSGA-Ⅱ的MCM散热-成本协同优化设计OA

As a key technology for improving the performance and integration density of integrated circuits,the layout design of Multi-Chip Module(MCM)packaging has a significant impact on system performance and cost.Traditional design methods relying on simula-tions and experiments are inefficient and difficult to achieve effective trade-offs in complex design spaces.This paper proposes a thermal performance and cost collaborative design strategy integrating automated simulation,surrogate models,and multi-objective optimization algorithms.First,Python secondary development was used to realize the full-process automation of numerical simulations,greatly im-proving simulation efficiency.Then,a high-precision surrogate model between chip spacing and maximum temperature was established,and fast prediction of thermal performance was achieved based on Support Vector Regression(SVR).On this basis,combined with the MCM cost model,the improved NSGA-II multi-objective algorithm was adopted to efficiently search for the Pareto optimal front.The research results show that the obtained Pareto front curve is superior to the original layout scheme,and the automated framework and intelligent algorithms significantly improve the efficiency of MCM collaborative design,providing a basis for the trade-off between ther-mal performance and cost.