用多模态智能体自动生成半导体缺陷报告，1分钟内完成
SemiFA: An Agentic Multi-Modal Framework for Autonomous Semiconductor Failure Analysis Report Generation
- 分四个智能体处理缺陷描述、根因分析、严重性评估和工艺建议
- 在A100 GPU上48秒生成完整报告，92.1%缺陷分类准确率
- 首次融合设备数据与视觉语言模型，适合芯片制造工程师使用
半导体失效分析（FA）需工程师查阅检测图像、关联设备遥测数据、参考历史缺陷记录并撰写结构化报告，每例耗时数小时。本文提出SemiFA，一个自主生成结构化FA报告的智能体多模态框架，可在一分钟内完成。SemiFA将FA分解为四智能体LangGraph流程：DefectDescriber利用DINOv2和LLaVA-1.6分类并描述缺陷形态；RootCauseAnalyzer融合SECS/GEM设备遥测数据与从Qdrant向量数据库检索的历史相似缺陷；SeverityClassifier评估严重性并估算良率影响；RecipeAdvisor提出工艺调整建议。第五节点生成PDF报告。我们构建了SemiFA-930数据集，包含930张标注的半导体缺陷图像及其结构化分析文本，涵盖九类缺陷，来源于流程合成、WM-811K和MixedWM38。DINOv2分类器在140张验证图像上达到92.1%准确率（宏平均F1=0.917），全管道在NVIDIA A100-SXM4-40 GB GPU上48秒生成完整报告。基于GPT-4o的评判实验显示，多模态融合使根因推理得分提升+0.86（1-5分制），设备遥测为关键模态。
原文摘要 · Abstract (English)
Semiconductor failure analysis (FA) requires engineers to examine inspection images, correlate equipment telemetry, consult historical defect records, and write structured reports, a process that can consume several hours of expert time per case. We present SemiFA, an agentic multi-modal framework that autonomously generates structured FA reports from semiconductor inspection images in under one minute. SemiFA decomposes FA into a four-agent LangGraph pipeline: a DefectDescriber that classifies and narrates defect morphology using DINOv2 and LLaVA-1.6, a RootCauseAnalyzer that fuses SECS/GEM equipment telemetry with historically similar defects retrieved from a Qdrant vector database, a SeverityClassifier that assigns severity and estimates yield impact, and a RecipeAdvisor that proposes corrective process adjustments. A fifth node assembles a PDF report. We introduce SemiFA-930, a dataset of 930 annotated semiconductor defect images paired with structured FA narratives across nine defect classes, drawn from procedural synthesis, WM-811K, and MixedWM38. Our DINOv2-based classifier achieves 92.1% accuracy on 140 validation images (macro F1 = 0.917), and the full pipeline produces complete FA reports in 48 seconds on an NVIDIA A100-SXM4-40 GB GPU. A GPT-4o judge ablation across four modality conditions demonstrates that multi-modal fusion improves root cause reasoning by +0.86 composite points (1-5 scale) over an image-only baseline, with equipment telemetry as the more load-bearing modality. To our knowledge, SemiFA is the first system to integrate SECS/GEM equipment telemetry into a vision-language model pipeline for autonomous FA report generation.
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