用于参数提取和缺陷检测的机器学习特征识别
Feature identification for parameter extraction and defect detection using machine learning
- Nearfield Instruments, B.V.（近场仪器有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一个通用框架，利用高速扫描探针显微镜图像训练机器学习网络，实现参数提取的特征检测与缺陷识别，以加速半导体工艺控制中的算法开发。
AI中文摘要：
先进半导体节点的工艺控制不仅在分辨率和吞吐量方面推动计量设备要求的极限，而且在可提取数据的丰富性方面也推动其极限，以使工程师能够微调工艺步骤以提高良率。向3D结构的转变要求从各层之间可能差异很大的结构中提取关键尺寸参数。对于在线工艺控制，必要的自动化促使开发针对特定层和设备的专用图像处理算法。同样，随着EUV时代随机缺陷的增加，在纳米尺度上检测缺陷需要识别低分辨率图像中捕获的特征，以满足高产量制造厂（HVM fab）的吞吐量要求，这同样可能导致定制算法的开发。随着基于机器学习的图像处理方法的出现，这两种情况下的算法开发过程都可以加速。在本工作中，我们提供了一个通用框架，在该框架下，从基于高速扫描探针显微镜的系统中获得的图像可用于训练网络，以实现参数提取的特征检测或缺陷识别。
英文摘要：
Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput but also in terms of the richness of data to be extracted to enable engineers to finetune the process steps for increased yield. The move towards 3D structures requires extraction of critical dimension parameters from structures which can vary largely from layer to layer. For in-line process control, the necessary automation forces the development of layer and equipment-specific dedicated image processing algorithms. Similarly, with the increase in stochastic defects in the EUV era, detection of defects at the nm scale requires the identification of features captured in low resolution to meet the throughput requirements of HVM fabs, which can again lead to custom algorithm development. With the emergence of ML-based image processing methods, this process of algorithm development for both cases can be accelerated. In this work, we provide the general framework under which the images obtained from high-speed scanning probe microscopy-based systems can be used to train a network for either feature detection for parameter extraction or defect identification.
