Multi-Agent AI System for Autonomous Semiconductor Test Optimization

Abstract

Semiconductor manufacturing generates large volumes of test, process, and equipment data that must be analyzed efficiently to ensure high yield, reliability, and operational efficiency. This paper presents an Explainable Multi-Agent AI Framework for Semiconductor Test Analytics and Optimization. The proposed framework employs a centralized orchestration mechanism to coordinate specialized agents for root cause analysis, yield optimization, predictive maintenance, and test strategy optimization. The system provides conversational query interaction, structured recommendations, confidence-aware reasoning, explainability tracing, and interactive analytics dashboards. Unlike conventional black-box AI approaches, the framework emphasizes transparency, interpretability, and deterministic reasoning, enabling engineers to understand, validate, and audit generated recommendations. A synthetic semiconductor manufacturing dataset was developed to evaluate the framework under realistic fabrication and testing scenarios. Experimental results demonstrate that the proposed framework effectively supports semiconductor diagnostics, process optimization, and decision-making while maintaining scalability, traceability, and operational transparency. The findings indicate that Explainable Multi-Agent Systems can serve as reliable decision-support tools for modern semiconductor manufacturing environments.