Wafer

Semiconductor manufacturing is a highly complex process requiring precise control and monitoring to maintain product quality and yield. This research presents a comprehensive comparative analysis of three machine learning algorithms—Random Forest, Support Vector Machine (SVM), and XGBoost—for anomaly detection in semiconductor fabrication. Through extensive experimentation using a real-world wafer dataset, we demonstrate that XGBoost outperforms other models, achieving 97.1\% accuracy, 96.4\% precision, and 95.0\% recall. The study further explores implementation challenges, real-time deployment considerations, and future directions including explainable AI approaches. Our findings establish a practical framework for implementing machine learning-based anomaly detection in semiconductor manufacturing environments, contributing to enhanced process control and yield optimization.

半导体制造是一项高度复杂的生产流程，需通过精准的控制与监测保障产品质量与生产良率。本研究针对半导体晶圆制造中的异常检测任务，对三类机器学习算法——随机森林（Random Forest）、支持向量机（Support Vector Machine, SVM）以及XGBoost——开展了全面的对比分析。通过基于真实晶圆数据集的大规模实验，我们证实XGBoost的性能优于其余模型，其准确率达97.1%、精确率为96.4%、召回率为95.0%。本研究还进一步探讨了算法落地的实践挑战、实时部署的关键考量因素，以及包含可解释人工智能（Explainable AI）在内的未来研究方向。本研究成果为在半导体制造场景中部署基于机器学习的异常检测系统构建了实用框架，有助于提升流程管控能力与生产良率优化效果。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于半导体制造异常检测的真实世界晶圆数据集，数据格式为.arff文件。它通过比较Random Forest、SVM和XGBoost等机器学习算法，支持异常检测研究，其中XGBoost模型取得了97.1%的准确率，有助于提升半导体生产过程的控制和良率优化。