The semiconductor industry is one of the most technology-evolving and capital-intensive market sectors. Effective inspection and metrology are necessary to improve product yield, increase product quality and reduce costs. In recent years, many semiconductor manufacturing equipments are equipped with sensors to facilitate real-time monitoring of the production process. These production-state and equipment-state sensor data provide an opportunity to practice machine-learning technologies in various domains, such as anomaly/fault detection, maintenance scheduling, quality prediction, etc. In this work, we focus on the task of soft sensing regression, which uses sensor data to predict impending inspection measurements that used to be measured in wafer inspection and metrology systems. We proposed an LSTM-based regressor and designed two loss functions for model training. Although engineers may look at our prediction errors in a subjective manner, a new piece-wise evaluation metric was proposed for assessing model accuracy in a mathematical way. The experimental results demonstrated that the proposed model can achieve accurate and early prediction of various types of inspections in complicated manufacturing processes.

翻译：半导体行业是技术更新最快且资本最密集的市场领域之一。有效的检测与计量对于提升产品良率、提高产品质量和降低成本至关重要。近年来，众多半导体制造设备配备了传感器，以实现生产过程的实时监控。这些反映生产状态和设备状态的传感器数据为在异常/故障检测、维护调度、质量预测等多个领域应用机器学习技术提供了契机。本研究聚焦于软感知回归任务，即利用传感器数据预测即将进行的检测测量值——这些测量值原本需通过晶圆检测与计量系统获得。我们提出了一种基于LSTM的回归器，并设计了两种损失函数用于模型训练。尽管工程师可能以主观方式看待预测误差，但本文提出了一种新的分段评估指标，以数学方式衡量模型精度。实验结果表明，所提出的模型能够针对复杂制造过程中的多种检测类型实现准确且提前的预测。