Presentation
SmartATPG: A Learning-based Automatic Test Pattern Generation with Graph Convolutional Network and Reinforcement Learning
DescriptionAutomatic test pattern generation (ATPG) is a critical technology in integrated circuit testing. It searches for effective test patterns 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 patterns is NP-complete. At the same time, the large amount of backtracking generated during the search for test patterns can directly affect the performance of ATPG. In this paper, a learning-based ATPG framework SmartATPG is proposed to search for high-quality test patterns, reduce the number of backtracking during the search process, and thereby improve the performance of ATPG. SmartATPG utilizes convolutional network (GCN) to fully extract circuit feature information and efficiently explore the ATPG search space through reinforcement learning (RL). Experimental results show that the proposed SmartATPG can perform better than traditional heuristic strategies and deep learning heuristic strategies on most benchmark circuits.
Event Type
Research Manuscript
TimeTuesday, June 2511:30am - 11:45am PDT
Location3010, 3rd Floor
EDA
Test, Validation and Silicon Lifecycle Management