Intelligent Assistants for the Semiconductor Failure Analysis with LLM-Based Planning Agents
This work addresses the problem of workflow automation for semiconductor failure analysis engineers, representing an incremental improvement by integrating existing AI components with a novel planning agent.
The paper tackled the challenge of orchestrating multiple AI models into cohesive workflows for semiconductor failure analysis by designing an LLM-based planning agent, which demonstrated operational effectiveness and reliability in supporting FA tasks.
Failure Analysis (FA) is a highly intricate and knowledge-intensive process. The integration of AI components within the computational infrastructure of FA labs has the potential to automate a variety of tasks, including the detection of non-conformities in images, the retrieval of analogous cases from diverse data sources, and the generation of reports from annotated images. However, as the number of deployed AI models increases, the challenge lies in orchestrating these components into cohesive and efficient workflows that seamlessly integrate with the FA process. This paper investigates the design and implementation of an agentic AI system for semiconductor FA using a Large Language Model (LLM)-based Planning Agent (LPA). The LPA integrates LLMs with advanced planning capabilities and external tool utilization, allowing autonomous processing of complex queries, retrieval of relevant data from external systems, and generation of human-readable responses. The evaluation results demonstrate the agent's operational effectiveness and reliability in supporting FA tasks.