A Transferable GNN-based Multi-Corner Performance Variability Modeling for Analog ICs

Abstract

Performance variability appears strong-nonlinear in analog ICs due to large process variations in advanced technologies. To capture such variability, a vast amount of data is required for learning-based accurate models. On the other hand, yield estimation across multiple PVT corners exacerbates data dimensionality further. In this paper, we propose a graph neural network (GNN)-based performance variability modeling method. The key idea is to leverage GNN techniques to extract variations-related local mismatch in analog circuits, and data efficiency is benefited by the ability of knowledge transfer among different PVT corners. Demonstrated upon three circuits in a commercial 65nm CMOS process and compared with the state-of-the-art modeling techniques, our method can achieve higher modeling accuracy while utilizing significantly less training data.