结合无监督与监督学习在显微成像中实现完整4H-SiC晶圆的缺陷分析
Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer
AI总结：
本文结合无监督与监督学习及数据挖掘技术，构建自动化图像分析流程，用于从约4万张图像拼接的KOH腐蚀4H-SiC晶圆显微图中提取全部缺陷的类型与位置。
摘要：
检测并分析半导体材料中的各类缺陷，是理解其潜在机制以及优化生产工艺的重要前提。对揭示缺陷的显微图像进行分析，通常需要分割和目标检测等图像分析任务。随着实验产生的数据量持续增长，人工处理这些任务变得越来越不可行。在本工作中，我们结合多种图像分析与数据挖掘技术，构建了一条稳健且准确的自动化图像分析流程。该流程能够从一张由约40,000张单独图像拼接而成的KOH腐蚀4H-SiC晶圆显微图像中，提取所有缺陷的类型与位置。
英文摘要：
Detecting and analyzing various defect types in semiconductor materials is an important prerequisite for understanding the underlying mechanisms as well as tailoring the production processes. Analysis of microscopy images that reveal defects typically requires image analysis tasks such as segmentation and object detection. With the permanently increasing amount of data that is produced by experiments, handling these tasks manually becomes more and more impossible. In this work, we combine various image analysis and data mining techniques for creating a robust and accurate, automated image analysis pipeline. This allows for extracting the type and position of all defects in a microscopy image of a KOH-etched 4H-SiC wafer that was stitched together from approximately 40,000 individual images.
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。
