通过模块化网络检测缺陷晶圆
Detecting Defective Wafers Via Modular Networks
AI总结：
针对缺陷晶圆检测中跨产品、跨工艺泛化不足的问题，提出按制造阶段组织的模块化网络MN，通过阶段模块组合预测KQI，提升检测通用性与可解释性。
摘要：
半导体制造过程中传感器的日益普及，使得利用数据驱动模型检测缺陷晶圆成为可能。这些传感器无需直接测量半导体器件的质量，而是捕捉不同传感器读数之间的模态，并可用于预测关键质量指标（KQI，例如粗糙度、电阻），以检测故障产品，从而显著降低维护物理计量步骤所需的资金和人力成本。然而，现有模型很少关注不同晶圆产品在各制造过程之间的相关性，并且通常面临泛化能力不足的问题。为实现通用故障检测，本文提出一种模块化网络（MN），该网络采用按制造阶段划分的时间序列数据集进行训练，并体现制造过程的结构。它将KQI预测分解为多个阶段模块的组合，以模拟组合式半导体制造过程，从而在不同晶圆类型和制造过程中普遍增强缺陷晶圆检测能力。大量实验证明了该方法的有效性，并阐明了这种组合式设计如何为更实际的应用提供可解释接口。
英文摘要：
The growing availability of sensors within semiconductor manufacturing processes makes it feasible to detect defective wafers with data-driven models. Without directly measuring the quality of semiconductor devices, they capture the modalities between diverse sensor readings and can be used to predict key quality indicators (KQI, \textit{e.g.}, roughness, resistance) to detect faulty products, significantly reducing the capital and human cost in maintaining physical metrology steps. Nevertheless, existing models pay little attention to the correlations among different processes for diverse wafer products and commonly struggle with generalizability issues. To enable generic fault detection, in this work, we propose a modular network (MN) trained using time series stage-wise datasets that embodies the structure of the manufacturing process. It decomposes KQI prediction as a combination of stage modules to simulate compositional semiconductor manufacturing, universally enhancing faulty wafer detection among different wafer types and manufacturing processes. Extensive experiments demonstrate the usefulness of our approach, and shed light on how the compositional design provides an interpretable interface for more practical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
