A recent technical article details a novel system for root cause analysis (RCA) in semiconductor manufacturing that combines ontology, GraphRAG, and LangGraph. The system is designed for a virtual fab environment, using an ontology to represent domain knowledge about wafer fabrication processes. GraphRAG enhances retrieval-augmented generation by leveraging graph structures to find relevant historical incidents and solutions. LangGraph then orchestrates a multi-step reasoning workflow, enabling the system to ask clarifying questions and iteratively narrow down root causes. This integration moves beyond simple keyword or vector search, providing a more context-aware and explainable diagnostic tool. For overseas developers and engineers, this represents a practical blueprint for applying advanced AI to complex industrial problems, particularly in high-stakes manufacturing where downtime is costly. The approach is extensible to other domains like automotive, aerospace, or energy, where similar root cause analysis challenges exist.
This article presents a system that integrates ontology, GraphRAG, and LangGraph for intelligent root cause analysis in a virtual semiconductor fab (RCA). It demonstrates how combining structured knowledge with LLM-based reasoning can accelerate fault diagnosis in manufacturing. The approach is relevant for any industrial setting requiring explainable and efficient root cause analysis.