Kla Corporation

Nurmohammed Patwary

Xiaochun Li

Bjorn Brauer

James A Smith

Kenong Wu

Richard Wallingford

Eugene Shifrin

Hong Chen

Hucheng Lee

Sangbong Park

Ramon Ynzunza

Qing Luo

Michael Cook

Vladimir Tumakov

Wei Si

Heonju Shin

Jusang Maeng

Leon Yu

Neil Troy

Systems and methods for detecting defects on a reticle are provided. One system is configured for generating different stacked difference images for multiple instances of first patterned areas in different rows on a wafer based on images generated for the first patterned areas in the different rows. The system is also configured for performing double detection based on the different stacked difference images. The system then identifies defects on the reticle based on the defects detected by the double detection. As described further herein, the systems and methods detect defects from multiple reticle rows printed on a wafer, which can reduce noise and enable detection of substantially small repeater defects. The embodiments are particularly useful for high sensitivity repeater defect detection for extreme ultraviolet (EUV) reticles and multi-die reticles (MDR).

Print check repeater defect detection

A sample characterization system is disclosed. In embodiments, the sample characterization system includes a controller communicatively coupled to an inspection sub-system, the controller including one or more processors configured to execute a set of program instructions stored in memory, the set of program instructions configured to cause the one or more processors to: acquire one or more target image frames of a sample; generate a target tensor with the one or more acquired target image frames; perform a first set of one or more decomposition processes on the target tensor to generate one or more reference tensors including one or more reference image frames; identify one or more differences between the one or more target image frames and the one or more reference image frames; and determine one or more characteristics of the sample based on the one or more identified differences.

System and method for inspection using tensor decomposition and singular value decomposition

Systems and methods for detecting defects on a reticle are provided. One system includes computer subsystem(s) configured for performing at least one repeater defect detection step in front-end processing during an inspection process performed on a wafer having features printed in a lithography process using a reticle. The at least one repeater defect detection step performed in the front-end processing includes identifying any defects detected at corresponding locations in two or more test images by double detection and any defects detected by stacked defect detection as first repeater defect candidates. One or more additional repeater defect detections may be performed on the first repeater defect candidates to generate final repeater defect candidates and identify defects on the reticle from the final repeater defect candidates.

An optical characterization system and a method of using the same are disclosed. The system comprises a controller configured to be communicatively coupled with one or more detectors configured to receive illumination from a sample and generate image data. One or more processors may be configured to receive images of dies on the sample, calculate dissimilarity values for all combinations of the images, perform a cluster analysis to partition the combinations of the images into two or more clusters, generate a reference image for a cluster of the two or more clusters using two or more of the combinations of the images in the cluster; and detect one or more defects on the sample by comparing a test image in the cluster to the reference image for the cluster.

Unsupervised learning-based reference selection for enhanced defect inspection sensitivity

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San Jose, CA, United States of America

Nurmohammed Patwary