论文标题
在直流电流压力下分析应力演化的时空神经网络
A Space-Time Neural Network for Analysis of Stress Evolution under DC Current Stressing
论文作者
论文摘要
由于连续的技术扩展，电气移民（EM）引起的可靠性问题（VLSI）电路引起了人们的关注。传统的EM模型通常会导致过于悲观的预测与未来技术节点的缩水设计余量不符。由神经网络在求解物理问题中的微分方程方面的最新成功的推动下，我们提出了一种新型的无网格模型，以计算VLSI电路中EM诱导的应力演化。该模型利用专门制作的时空物理信息神经网络（STPINN）作为EM分析的求解器。通过将基于物理的EM分析与融合焦耳加热的动态温度耦合，我们可以在恒定，依赖时间和时空依赖性温度下观察到沿多段互连树的应力演化。提出的STPINN方法避免了常规数值应力演化分析所需的时间离散化和网格，并提供了大量的计算节省。与竞争方案的数值比较表明了2x〜52x的速度，精度令人满意。
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.
