公开(公告)号：US12211196B2

公开(公告)日：2025-01-28

申请号：US18305287

申请日：2023-04-21

Applicant: KLA Corporation

Inventor： Kuljit S. Virk ,  Vera Andreeva ,  Lawrence Muray

IPC: G06T7/00 ,  G06V10/774 ,  G06V10/776 ,  G06V20/70

Abstract: Methods and systems for detecting defects in images of a specimen are provided. One system includes a computer subsystem configured for training an ensemble of deep learning models by altering one or more parameters of the ensemble until a pseudo-loss function determined based on output of the ensemble is approximately equal to but not greater than 0.5. The computer subsystem is also configured for detecting defects in runtime specimen images by inputting the runtime specimen images into the trained ensemble and generating runtime labels for the runtime specimen images indicating if a defect has been detected in the runtime specimen images based on outputs of the deep learning models in the trained ensemble.

公开(公告)号：US20240354925A1

公开(公告)日：2024-10-24

申请号：US18305287

申请日：2023-04-21

Applicant: KLA Corporation

Inventor： Kuljit S. Virk ,  Vera Andreeva ,  Lawrence Muray

IPC: G06T7/00 ,  G06V10/774 ,  G06V10/776 ,  G06V20/70

CPC classification number: G06T7/0004 ,  G06V10/774 ,  G06V10/776 ,  G06V20/70 ,  G06T2207/10061 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30148

Abstract: Methods and systems for detecting defects in images of a specimen are provided. One system includes a computer subsystem configured for training an ensemble of deep learning models by altering one or more parameters of the ensemble until a pseudo-loss function determined based on output of the ensemble is approximately equal to but not greater than 0.5. The computer subsystem is also configured for detecting defects in runtime specimen images by inputting the runtime specimen images into the trained ensemble and generating runtime labels for the runtime specimen images indicating if a defect has been detected in the runtime specimen images based on outputs of the deep learning models in the trained ensemble.