Physics-Constrained Multi-Agent Automation for Design-for-Reliability

Abstract

Semiconductor Design-for-Reliability (DfR) faces a critical knowledge bottleneck. Experimental parameters, failure criteria, and mechanism interpretations remain scattered across publications in inconsistent formats. Such fragmentation hinders systematic reuse and cross-study comparison. To address this challenge, we present an automated framework that extracts reliability data from unstructured technical documents and transforms it into standardized, auditable knowledge for experiment planning. Coordinated AI modules perform data extraction, lifetime and degradation modeling, physical constraint validation, and test condition recommendation. All intermediate results are stored in a shared database for traceability. Local deployment of all AI models ensures data confidentiality suitable for industrial use without reliance on external cloud services. Evaluation on a corpus of 50 IGBT power-cycling publications showed that the extraction module achieved an F1 score of 0.83, which outperformed rule-based methods by 15 percentage points. The largest gains were observed for semantically variable parameters such as failure criteria and Weibull coefficients. For experiment design recommendations, alignment with published test matrices reached up to 62.5%, compared to 0-25% for general-purpose AI models of comparable or greater scale. These results demonstrate that domain-structured knowledge integration, rather than AI model size alone, drives the performance improvement and offers a practical pathway for auditable, privacy-preserving automation in reliability engineering workflows.