基于神经网络的硅通孔电热瞬态优化方法

Transient electrothermal coupling optimization method of through-silicon via based on neural network

针对三维集成微系统中高密度集成导致的热效应和复杂的多物理场耦合问题,提出了一种基于神经网络辅助人工蜂群的硅通孔电热瞬态优化方法,用于高效准确地分析三维微系统中硅通孔阵列的瞬态电热问题.利用有限元分析软件进行了电热耦合协同仿真,分析了设计参数(硅通孔半径、氧化物厚度、硅通孔间距)对硅通孔阵列中铜柱温度、微凸点温度等性能的影响.利用神经网络建立了设计参数与性能参数之间的映射关系.提出了一种具有性能约束的协同优化策略,并采用蜂群优化算法对设计参数进行优化.根据优化后的设计参数,有限元模拟结果与预测性能基本一致,结构的最高温度误差为2.6%.结论不仅证明了优化策略的可行性,且与传统有限元方法相比,该优化设计方法极大地缩短了仿真时间,简化了多场耦合中复杂数学分析.

Based on neural network-assisted artificial bee colony,a silicon through-hole electrothermal transient optimization method is introduced.It efficiently and accurately analyzes transient electrothermal issues in three-dimensional microsystems.Electrothermal coupling simulations are conducted using finite element analysis software to examine the impact of design parameters(silicon through-hole radius,oxide thickness,silicon through-hole spacing)on silicon throughhole array performance,including copper pillar and micro-protrusion temperatures.A neural network is used to establish the mapping relationship between design and performance parameters.A collaborative optimization strategy with performance constraints is proposed to optimize design parameters using a bee colony optimization algorithm.The predicted performance closely matches finite element simulation results,with a maximum temperature deviation of 2.6%.This validates the feasibility of the optimization strategy.This method significantly reduces simulation time and simplifies mathematical analysis in multi-physics coupling compared to traditional finite element methods.