Analog IC Aging-induced Degradation Estimation via Heterogeneous Graph Convolutional Networks.

With continued scaling, transistor aging induced by Hot Carrier Injection and Bias Temperature Instability causes a gradual failure of nanometer-scale integrated circuits (ICs). In this paper, to characterize the multi-typed devices and connection ports, a heterogeneous directed multigraph is adopted to efficiently represent analog IC post-layout netlists. We investigate a heterogeneous graph convolutional network (H-GCN) to fast and accurately estimate aging-induced transistor degradation. In the proposed H-GCN, an embedding generation algorithm with a latent space mapping method is developed to aggregate information from the node itself and its multi-typed neighboring nodes through multi-typed edges. Since our proposed H-GCN is independent of dynamic stress conditions, it can replace static aging analysis. We conduct experiments on very advanced 5nm industrial designs. Compared to traditional machine learning and graph learning methods, our proposed H-GCN can achieve more accurate estimations of aging-induced transistor degradation. Compared to an industrial reliability tool, our proposed H-GCN can achieve 24.623× speedup on average.