计算机科学
炸薯条
过程(计算)
倒装芯片
实验设计
压力(语言学)
集成电路设计
发电机(电路理论)
机器学习
嵌入式系统
功率(物理)
统计
图层(电子)
化学
有机化学
哲学
物理
操作系统
电信
胶粘剂
量子力学
语言学
数学
标识
DOI:10.1109/icicm54364.2021.9660367
摘要
The development of fine-linewidth semiconductor manufacturing process imposes additional requirements on the design optimization. This paper proposes and validates a simulation driven design methodology for structural design optimization of chip package integration. Finite Element Analysis method is employed to perform stress simulation for chip packages and then serves as a training dataset generator for machine learning model development. The effects of chip design parameters on the first principal stress are studied. Multiple machine learning algorithms are applied and evaluated as surrogate models for stress prediction. The random forest algorithm is identified to have the best capability to perform stress prediction for chip package integration design.
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