公开(公告)号：US20230418995A1

公开(公告)日：2023-12-28

申请号：US18339973

申请日：2023-06-22

申请人： Onto Innovation Inc.

发明人： Jie Li ,  Wei Ming Chiew ,  Pei Fen Teh ,  Jingsheng Shi

IPC分类号： G06F30/20

CPC分类号： G06F30/20

摘要： Physical modeling and machine learning modeling are combined to analyze signals from multiple data sources, including metrology data acquired from different tool sets or at different process steps, and data related to processing equipment, such as sensor data, process parameters, Advanced Process Control (APC) parameters, context data, etc. At least one physical model is generated and used to analyze metrology signals from metrology tools to extract measurement results for key and non-key parameters of a structure on a sample. At least one machine learning model is built and trained to predict parameters of interest based on the extracted measurement results as well as additional data, including raw measured signals, reference data and/or design of experiment (DOE) data, and data from different tool sets or the same tool as used for the physical modeling.

公开(公告)号：US20240159656A1

公开(公告)日：2024-05-16

申请号：US18503783

申请日：2023-11-07

申请人： Onto Innovation Inc.

发明人： Pei Fen Teh ,  Rajaram Attukur Nandagopal ,  Youxian Wen

IPC分类号： G01N21/21

CPC分类号： G01N21/211

摘要： Complex three-dimensional structures in semiconductor devices are measured using Mueller matrix paired off-diagonal elements to generate machine learning predictions of asymmetric parameters of the device and determine dimensional parameters based on one or more Mueller matrix elements and the asymmetric parameters. The measurements of the device may be performed at different azimuth angles selected based on sensitivity to the asymmetric parameters and the dimensional parameters. Additionally, the Mueller matrix elements may be generated based on measurements performed at azimuth angles that are 180° apart to eliminate asymmetric noise from the measurement tool. One or more models of the device may be used with the Mueller matrix elements to generate dimensional parameter information and optionally preliminary asymmetrical parameters. The determined asymmetric parameters may be fed forward to the one or more models for determining the dimensional parameters to suppress a correlation between dimensional parameters and asymmetric parameters of the device.