Multilayer Perceptron Based Stress Evolution Analysis under DC Current Stressing for Multi-segment Wires

TL;DR

This paper introduces a neural network-based method using multilayer perceptrons to accurately predict stress evolution in VLSI interconnects under electromigration, offering improved accuracy and efficiency over traditional numerical methods.

Contribution

It presents a novel physics-informed neural network approach for stress analysis in interconnects, eliminating the need for meshing and reducing computational iterations.

Findings

Enhanced accuracy over FEM-based methods

Reduced computational time and complexity

Effective modeling of stress evolution under time-varying conditions

Abstract

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 (IC) 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 paper, we propose an approach using multilayer perceptrons (MLP) 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 FEM. Numerical results confirm its advantages on accuracy and computational performance.