计量学
临界尺寸
计算机科学
维数(图论)
约束(计算机辅助设计)
集合(抽象数据类型)
周转时间
数据集
过程(计算)
半导体器件制造
机器学习
计算机工程
可靠性工程
人工智能
工程类
数学
电气工程
光学
机械工程
统计
物理
薄脆饼
纯数学
程序设计语言
操作系统
作者
Franklin J. Wong,Yudong Hao,Wenmei Ming,Petar Žuvela,Peifen Teh,Jingsheng Shi,Jie Li
出处
期刊:Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV
日期:2021-02-22
摘要
With the aggressive scaling of semiconductor devices, the increasing complexity of device structure coupled with tighter metrology error budget has driven up Optical Critical Dimension (OCD) time to solution to a critical point. Machine Learning (ML), thanks to its extremely fast turnaround, has been successfully applied in OCD metrology as an alternative solution to the conventional physical modeling. However, expensive and limited reference data or labeled data set necessary for ML to learn from often leads to under- or overlearning, limiting its wide adoption. In this paper, we explore techniques that utilize process information to supplement reference data and synergizing physical modeling with ML to prevent under- or overlearning. These techniques have been demonstrated to help overcome the constraint of limited reference data with use cases in challenging OCD metrology for advanced semiconductor nodes.
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