用机器学习加速半导体制造中的参数提取与缺陷检测
Feature identification for parameter extraction and defect detection using machine learning
半导体厂的朋友可以看看，讲的是怎么用显微镜图像训练网络，一套框架同时搞定关键尺寸提取和 nm 级缺陷检测。
这篇论文面向先进制程节点的在线过程控制，提出一个通用框架，用高速扫描探针显微镜采集的图像训练网络。框架支持两类任务：一是从逐层差异较大的 3D 结构中提取关键尺寸参数，二是在 EUV 时代随机缺陷增多的情况下识别 nm 级缺陷。作者指出传统方式需要针对每层和每台设备单独开发图像处理算法，ML 方法可以加速这一开发过程。低分辨率下的特征识别方案也兼顾了 HVM 产线的吞吐量要求。
Feature identification for parameter extraction and defect detection using machine learning
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.