用大模型智能体自动完成半导体故障分析全流程
Intelligent Assistants for the Semiconductor Failure Analysis with LLM-Based Planning Agents
- 基于大模型规划代理，自主调度多个AI工具
- 可处理复杂查询并从外部系统检索数据
- 适合半导体研发与质检团队快速部署
故障分析（FA）是一项高度复杂且依赖知识的过程。在FA实验室的计算基础设施中集成AI组件，有望自动化图像异常检测、跨数据源案例检索以及基于标注图像的报告生成等任务。然而，随着部署的AI模型数量增加，如何将这些组件整合为连贯高效的流程，无缝融入FA工作流，成为挑战。本文研究并实现了一种基于大语言模型（LLM）的规划代理（LPA）的智能体系统，用于半导体故障分析。该LPA结合了先进的规划能力与外部工具调用功能，能够自主处理复杂查询，从外部系统检索相关信息，并生成可读的人类响应。评估结果表明，该代理在支持FA任务方面具备良好的操作有效性与可靠性。
原文摘要 · Abstract (English)
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.
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