电迁移
计算机科学
超大规模集成
互连
感知器
有限元法
缩放比例
人工神经网络
可靠性(半导体)
集成电路
代表(政治)
算法
计算科学
数学
工程类
人工智能
物理
电气工程
结构工程
操作系统
计算机网络
量子力学
政治学
政治
嵌入式系统
功率(物理)
法学
几何学
作者
Tianshu Hou,Peining Zhen,Ngai Wong,Quan Chen,Guoyong Shi,S. G. Wang,Hai‐Bao Chen
标识
DOI:10.1109/tcad.2022.3176545
摘要
Electromigration (EM) is one of the major concerns in the reliability analysis of very large-scale integration (VLSI) systems due to the continuous technology scaling. Accurately predicting the time-to-failure of integrated circuits (ICs) becomes increasingly important for modern IC design. However, traditional methods are often not sufficiently accurate, leading to undesirable over-design especially in advanced technology nodes. In this article, we propose an approach using multilayer perceptrons (MLPs) to compute stress evolution in the interconnect trees during the void nucleation phase. The availability of a customized trial function for neural network training holds the promise of finding dynamic mesh-free stress evolution on complex interconnect trees under time-varying temperatures. Specifically, we formulate a new objective function considering the EM-induced coupled partial differential equations (PDEs), boundary conditions (BCs), and initial conditions to enforce the physics-based constraints in the spatial–temporal domain. The proposed model avoids meshing and reduces temporal iterations compared with conventional numerical approaches like finite element method. Numerical results confirm its advantages on accuracy and computational performance.
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