A Space-Time Neural Network for Analysis of Stress Evolution under DC Current Stressing

TL;DR

This paper introduces a novel space-time physics-informed neural network (STPINN) model for analyzing electromigration-induced stress evolution in VLSI circuits, offering faster computation and accurate predictions without traditional meshing.

Contribution

The paper presents a mesh-free, neural network-based approach for EM stress analysis that integrates temperature effects and eliminates the need for time discretization, improving efficiency.

Findings

Achieves 2x to 52x speedup over traditional methods.

Accurately models stress evolution under various temperature conditions.

Eliminates meshing and time discretization in stress analysis.

Abstract

The electromigration (EM)-induced reliability issues in very large scale integration (VLSI) circuits have attracted increased attention due to the continuous technology scaling. Traditional EM models often lead to overly pessimistic prediction incompatible with the shrinking design margin in future technology nodes. Motivated by the latest success of neural networks in solving differential equations in physical problems, we propose a novel mesh-free model to compute EM-induced stress evolution in VLSI circuits. The model utilizes a specifically crafted space-time physics-informed neural network (STPINN) as the solver for EM analysis. By coupling the physics-based EM analysis with dynamic temperature incorporating Joule heating and via effect, we can observe stress evolution along multi-segment interconnect trees under constant, time-dependent and space-time-dependent temperature during the void nucleation phase. The proposed STPINN method obviates the time discretization and meshing required in conventional numerical stress evolution analysis and offers significant computational savings. Numerical comparison with competing schemes demonstrates a 2x ~ 52x speedup with a satisfactory accuracy.