Machine Learning-Driven STL Generation for Enhancing Functional Safety of E/E Systems
Sanjay Kumar Das, Swastik Bhattacharya, Anand Menon, Shamik Kundu, Pooja Madhusoodhanan, Prasanth Viswanathan Pillai, Rubin Parekhji, Arnab Raha, Suvadeep Banerjee, Suriyaprakash Natarajan, Kanad Basu · 2025
The increasing complexity of safety-critical hardware systems demands advanced methods for ensuring functional safety (FuSa). Traditional techniques like ATPG and BIST are intrusive, requiring additional hardware and disrupting operations, making them unsuitable for in-field testing. To address this, for the first time, we propose a machine learning (ML)-driven automated Self-Test Library (STL) generation for seamless in-field testing during idle periods, ensuring uninterrupted fault detection and high system performance. Utilizing reinforcement learning, the STL generates design-specific test patterns, achieving up to $57.57 \%$ improvement in fault coverage and up to $85 \%$ efficiency compared to existing pattern-based testing, enhancing FuSa in mission-critical applications.