公开(公告)号：US10599951B2

公开(公告)日：2020-03-24

申请号：US16364140

申请日：2019-03-25

申请人： KLA-Tencor Corporation

发明人： Kris Bhaskar ,  Laurent Karsenti ,  Brad Ries ,  Lena Nicolaides ,  Richard (Seng Wee) Yeoh ,  Stephen Hiebert

IPC分类号： G06K9/62

摘要： Methods and systems for training a neural network for defect detection in low resolution images are provided. One system includes an inspection tool that includes high and low resolution imaging subsystems and one or more components that include a high resolution neural network and a low resolution neural network. Computer subsystem(s) of the system are configured for generating a training set of defect images. At least one of the defect images is generated synthetically by the high resolution neural network using an image generated by the high resolution imaging subsystem. The computer subsystem(s) are also configured for training the low resolution neural network using the training set of defect images as input. In addition, the computer subsystem(s) are configured for detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network.

公开(公告)号：US20190303717A1

公开(公告)日：2019-10-03

申请号：US16364140

申请日：2019-03-25

申请人： KLA-Tencor Corporation

发明人： Kris Bhaskar ,  Laurent Karsenti ,  Brad Ries ,  Lena Nicolaides ,  Richard (Seng Wee) Yeoh ,  Stephen Hiebert

IPC分类号： G06K9/62

摘要： Methods and systems for training a neural network for defect detection in low resolution images are provided. One system includes an inspection tool that includes high and low resolution imaging subsystems and one or more components that include a high resolution neural network and a low resolution neural network. Computer subsystem(s) of the system are configured for generating a training set of defect images. At least one of the defect images is generated synthetically by the high resolution neural network using an image generated by the high resolution imaging subsystem. The computer subsystem(s) are also configured for training the low resolution neural network using the training set of defect images as input. In addition, the computer subsystem(s) are configured for detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network.