标题： 智能工业中半导体制造的罕见类别预测模型 显示英文标题

标题： Rare Class Prediction Model for Smart Industry in Semiconductor Manufacturing

摘要： 工业的发展使得物理系统和数字系统的集成成为可能，促进了对制造过程的大量数据收集。 这种集成提供了一种可靠的方法来提高工艺质量和管理设备健康状况。 然而，从实际制造过程中收集的数据通常表现出一些具有挑战性的特性，例如严重的类别不平衡、高缺失值率和噪声特征，这阻碍了有效的机器学习实施。 在本研究中，开发了一种针对从智能半导体制造过程中收集的现场数据的罕见类别预测方法。 主要目标是构建一个能够解决噪声和类别不平衡问题的模型，以增强类别分离。 与现有文献相比，所开发的方法展示了有希望的结果，这将允许预测新的观测结果，从而为未来的维护计划和生产质量提供见解。 该模型使用各种性能指标进行了评估，ROC曲线显示AUC为0.95，精确度为0.66，召回率为0.96 显示英文摘要

摘要： The evolution of industry has enabled the integration of physical and digital systems, facilitating the collection of extensive data on manufacturing processes. This integration provides a reliable solution for improving process quality and managing equipment health. However, data collected from real manufacturing processes often exhibit challenging properties, such as severe class imbalance, high rates of missing values, and noisy features, which hinder effective machine learning implementation. In this study, a rare class prediction approach is developed for in situ data collected from a smart semiconductor manufacturing process. The primary objective is to build a model that addresses issues of noise and class imbalance, enhancing class separation. The developed approach demonstrated promising results compared to existing literature, which would allow the prediction of new observations that could give insights into future maintenance plans and production quality. The model was evaluated using various performance metrics, with ROC curves showing an AUC of 0.95, a precision of 0.66, and a recall of 0.96