Reinforcement-Learning-Based Test Program Generation for Software-Based Self-Test

Abstract

Software-based Self-test (SBST) has been recognized as a promising complement to scan-based structural Built-in Self-test (BIST), especially for in-field self-test applications. In response to the ever-increasing complexities of the modern CPU designs, machine learning algorithms have been proposed to extract processor behavior from simulation data and help constrain ATPG to generate functionally-compatible patterns. However, these simulation-based approaches in general suffer sample inefficiency, i.e., only a small portion of the simulation traces are relevant to fault detection. Inspired by the recent advances in reinforcement learning (RL), we propose an RL-based test program generation technique for transition delay fault (TDF) detection. During the training process, knowledge learned from the simulation data is employed to tune the simulation policy; this close-loop approach significantly improves data efficiency, compared to previous open-loop approaches. Furthermore, RL is capable of dealing with delayed responses, which is common when executing processor instructions. Using the trained RL model, instruction sequences that bring the processor to the fault-sensitizing states, i.e., TDF test patterns, can be generated. The proposed test program generation technique is applied to a MIPS32 processor. For TDF, the fault coverage is 94.94%, which is just 2.57% less than the full-scan based approach.