Kla Corporation

Kedar Grama

Bjorn Brauer

Richard Wallingford

Hucheng Lee

Sangbong Park

Santosh Kumar

Methods and systems for determining information for a specimen are provided. The embodiments described herein are configured for training a machine learning (ML) model for generating higher resolution images of a specimen from images of the specimen generated by an inspection subsystem. The training includes a pre-training step that is performed using only simulated images and a re-training step that is performed using actual images of a test specimen. The higher resolution images generated by the trained ML model from lower resolution inspection images can be used for applications to including nuisance filtering and defect classification.

Training a machine learning model to generate higher resolution images from inspection images

One or more semiconductor wafers or portions thereof are scanned using a primary optical mode, to identify defects. A plurality of the identified defects, including defects of a first class and defects of a second class, are selected and reviewed using an electron microscope. Based on this review, respective defects of the plurality are classified as defects of either the first class or the second class. The plurality of the identified defects is imaged using a plurality of secondary optical modes. One or more of the secondary optical modes are selected for use in conjunction with the primary optical mode, based on results of the scanning using the primary optical mode and the imaging using the plurality of secondary optical modes. Production semiconductor wafers are scanned for defects using the primary optical mode and the one or more selected secondary optical modes.

Growing community of inventors

Fremont, CA, United States of America

Kedar Grama