SmartATPG: Learning-based Automatic Test Pattern Generation with Graph Convolutional Network and Reinforcement Learning

Automatic test pattern generation (ATPG) is a critical technology in integrated circuit testing. It searches for effective test vectors to detect all possible faults in the circuit as entirely as possible, thereby ensuring chip yield and improving chip quality. However, the process of searching for test vectors is NP-complete. At the same time, the large amount of backtracking generated during the search for test vectors can directly affect the performance of ATPG. In this paper, a learning-based ATPG framework, SmartATPG, is proposed to search for high-quality test vectors, reduce the number of backtracking during the search process, and thereby improve the performance of ATPG. SmartATPG utilizes graph convolutional network (GCN) to fully extract circuit feature information and efficiently explore the ATPG search space through reinforcement learning (RL). Experimental results indicate that the proposed SmartATPG outperforms traditional and artificial neural network (ANN)-based heuristic strategies on most benchmark circuits.