RDT: An Optimized Automatic Test Pattern Generation Method Based on Robust Decision Tree

Automatic Test Pattern Generation (ATPG) is a crucial technology in digital circuit testing. However, as circuit complexity increases, traditional methods face significant challenges, including inefficiency and resource waste caused by excessive backtracking. This paper presents an optimized ATPG method based on robust decision trees to mitigate backtracking and improve overall performance. The approach involves constructing a decision tree to learn the rich historical backtracking data generated during the FAN ATPG process, incorporating a negative exponential loss function to reduce the impact of noisy data on model quality. The trained model is then used to guide backtracking decisions in ATPG, thereby enhancing efficiency. Experimental results on ISCAS89 and ISCAS85 benchmark circuits demonstrate that, compared to deep learning-optimized ATPG methods, the proposed approach reduces backtracking frequency by 24% and test pattern count by 25%, while maintaining comparable test quality.