UC Riverside

The reliability of Very Large Scale Integration (VLSI) circuits is crucial in modern electronic devices. VLSI circuits, which contain millions of transistors, are vulnerable to a variety of reliability issues such as electromigration (EM), time-dependent dielectric breakdown (TDDB), and temperature variation. These issues can lead to circuit failure and reduce the lifetime of electronic devices. Traditionally, VLSI reliability analysis and prediction have been performed using physics-based models and simulators. These models, however, are computationally intensive and can be time-consuming to run. In recent years, machine learning (ML) techniques have been used to predict and diagnose reliability issues in VLSI circuits. This thesis presents an in-depth study of machine learning techniques applied to EM analysis, post-silicon thermal map estimation, and electrostatics analysis. Specifically, the first segment proposes two data-driven ML methods for the fast prediction of transient EM stress of general interconnects in VLSI circuits. The traditional numerical partial differential equation (PDE) problem is treated as an image processing or graph aggregation problem which yields considerable speedup with acceptable accuracy. However, these methods are still supervised learning approaches, requiring extensive training data generated from numerical solvers. Therefore, the second segment proposes a hierarchical physics-informed neural networks (PINN) based method for EM analysis. This approach leverages PINN, which is trained mainly by physics laws with minimal labeled data requirements. The hierarchical nature of interconnects is leveraged, and the entire interconnect tree is solved step by step. Temperature variation has always been problematic in VLSI circuits, as reliability degrades drastically as temperature varies. The third segment presents a real-time thermal map estimation method for commercial VLSI circuits. This approach treats thermal modeling as an image-generation task using generative neural networks (GANs), producing tool-accurate thermal map estimations. Electrostatics analysis is an essential step for analyzing TDDB, an important failure mechanism for interconnects. Lastly, the fourth segment presents a PINN-based 2D electric field analysis method. This approach eliminates the heavy dependence of finite element methods (FEM) used in traditional TDDB analysis and leads to orders of magnitude of speedup.