Kla Tencor Corporation

Kla Corporation

Bjorn Brauer

Hucheng Lee

Sangbong Park

Lisheng Gao

Xiaochun Li

Richard Wallingford

Santosh Bhattacharyya

Eugene Shifrin

James A Smith

Nurmohammed Patwary

Ashok Mathew

Hong Chen

Scott Allen Young

Laurent Karsenti

Vaibhav Gaind

Bryant Mantiply

Sreeram Chandrasekaran

Benjamin Murray

Vladimir Tumakov

Sumit Sen

Abdurrahman Sezginer

Ashok V Kulkarni

Kenong Wu

Eliezer Rosengaus

Kris Bhaskar

Mohan Mahadevan

Jing Zhang

Mark J Roulo

Saibal Banerjee

Junqing Huang

Erfan Soltanmohammadi

Ge Cong

Soren Konecky

John Raymond Jordan, Iii

Chetana Bhaskar

Christopher Maher

Vijayakumar Ramachandran

Huan Jin

Shishir Suman

Ramon Ynzunza

Ravikumar Sanapala

Boshi Huang

Vijay Ramachandran

Kedar Grama

Karen Biagini

Gary Taan

Qing Luo

Michael Cook

Wei Si

Leon Yu

David Dowling

Tarunark Singh

Neil Troy

Jingshan Zhong

Roni Miller

Ravichander Rao

Andreas Russ

Roger Davis

Swati Ramanathan

Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem. The one or more components include a semantic segmentation model configured for assigning labels to each of multiple pixels in an image responsive to what is represented in each of the multiple pixels. The image is an image of a specimen generated by an imaging subsystem. The computer subsystem is configured for determining information for the specimen from the assigned labels and without a reference image for the specimen.

Semantic image segmentation for semiconductor-based applications

Wafer-to-wafer and within-wafer image contrast variations can be identified and mitigated by extracting an image frame during recipe setup and then during runtime at the same location. Image contrast is determined for the two image frames. A ratio of the contrast for the two image frames can be used to determine contrast variations and focus variation.

Image contrast metrics for deriving and improving imaging conditions

Methods and systems for deep learning alignment for semiconductor applications are provided. One method includes transforming first actual information for an alignment target on a specimen from either design data to a specimen image or a specimen image to design data by inputting the first actual information into a deep generative model such as a GAN. The method also includes aligning the transformed first actual information to second actual information for the alignment target, which has the same information type as the transformed first actual information. The method further includes determining an offset between the transformed first actual information and the second actual information based on results of the aligning and storing the determined offset as an align-to-design offset for use in a process performed on the specimen.

Deep generative model-based alignment for semiconductor applications

Methods and systems for generating a simulated image for a specimen are provided. One system includes one or more computer subsystems and one or more components executed by the one or more computer subsystems. The one or more components include a generative adversarial network (GAN), e.g., a conditional GAN (cGAN), trained with a training set that includes portions of design data for one or more specimens designated as training inputs and corresponding images of the one or more specimens designated as training outputs. The one or more computer subsystems are configured for generating a simulated image for a specimen by inputting a portion of design data for the specimen into the GAN.

Generative adversarial networks (GANs) for simulating specimen images

Methods and systems for setting up inspection of a specimen are provided. One system includes one or more computer subsystems configured for acquiring a reference image for a specimen and modifying the reference image to fit the reference image to a design grid thereby generating a golden grid image. The one or more computer subsystems are also configured for storing the golden grid image for use in inspection of the specimen. The inspection includes aligning a test image of the specimen generated from output of an inspection subsystem to the golden grid image.

Setting up inspection of a specimen

Common events between layers on a semiconductor wafer are filtered. Common events should contain the majority of defects of interest. Only nuisance events that are common between layers on the semiconductor wafer remain, which reduces the nuisance rate. Defects that are common across layers can be filtered based on, for example, defect coordinates, a difference image, or defect attributes.

Cross layer common-unique analysis for nuisance filtering

Methods and systems for selecting defect detection methods for inspection of a specimen are provided. One system includes one or more computer subsystems configured for separating polygons in a care area into initial sub-groups based on a characteristic of the polygons on the specimen and determining a characteristic of noise in output generated by a detector of an inspection subsystem for the polygons in the different initial sub-groups. The computer subsystem(s) are also configured for determining final sub-groups for the polygons by combining any two or more of the different initial sub-groups having substantially the same values of the characteristic of the noise. In addition, the computer subsystem(s) are configured for selecting first and second defect detection methods for application to the output generated by the detector of the inspection subsystem during inspection of the specimen or another specimen.

Selecting defect detection methods for inspection of a specimen

A care area is determined in an image of a semiconductor wafer. The care area is divided into sub-care areas based on the shapes of polygons in a design file associated with the care area. A noise scan of a histogram for the sub-care areas is then performed. The sub-care areas are clustered into groups based on the noise scan of the histogram.

Clustering sub-care areas based on noise characteristics

Methods and systems for aligning images of a specimen generated with different modes of an imaging subsystem are provided. One method includes separately aligning first and second images generated with first and second modes, respectively, to a design for the specimen. For a location of interest in the first image, the method includes generating a first difference image for the location of interest and the first mode and generating a second difference image for the location of interest and the second mode. The method also includes aligning the first and second difference images to each other and determining information for the location of interest from results of the aligning.

Multi-imaging mode image alignment

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

Methods and systems for controlling a process for inspection of a specimen are provided. One system includes one or more computer subsystems configured for determining a statistical characteristic of difference images generated for multiple instances of a care area on a specimen and determining variation in the statistical characteristic compared to a statistical characteristic of difference images generated for multiple instances of the care area on one or more other specimens. In addition, the one or more computer subsystems are configured for determining one or more changes to one or more parameters used for detecting defects in the care area on the specimen based on the variation.

Controlling a process for inspection of a specimen

Methods and systems for selecting mode(s) for inspection of specimens are provided. One method includes statistically predicting if data points in a set correspond to defects or nuisances on a specimen. The data points include attribute(s) determined for discrete locations on the specimen from output generated by two or more modes of an inspection system. Events have been detected at the discrete locations with at least one of the modes. The method also includes determining a quantitative measure for each of two or more different combinations of the modes thereby determining different quantitative measures. The quantitative measure for each of the different combinations is responsive to how well one of the combinations detects the defects and minimizes detection of the nuisances. The method further includes selecting one or more of the modes for inspection of specimens of the same type as the specimen based on the determined quantitative measures.

Statistical learning-based mode selection for multi-mode inspection

Methods and systems for selecting a mode of a tool used for a process performed on a specimen are provided. One system includes one or more computer subsystems and one or more components executed by the one or more computer subsystems. The one or more components include a generative adversarial network (GAN), e.g., a conditional GAN (cGAN). The computer subsystem(s) are configured for modifying a portion of design data for a specimen to generate an artificially defective portion of the design data and generating simulated images for the specimen by inputting the portion of the design data and the artificially defective portion of the design data into the GAN. The computer subsystem(s) are also configured for determining one or more characteristics of the simulated images and selecting a mode of a tool used for a process performed on the specimen based on the determined one or more characteristics.

Deep generative models for optical or other mode selection

Methods and systems for determining one or more alignment parameters for use in a process performed on a specimen are provided. One method includes determining measures of similarity between images generated by an imaging system for corresponding locations in each of two or more pairs of dies on a specimen and performing cluster analysis based on the determined measures of similarity to identify the images that are most similar to each other and to assign different subsets of the images that are most similar to each other to different die clusters, respectively. The method also includes separately determining one or more alignment parameters for two or more of the different die clusters. The one or more alignment parameters are used for aligning images generated by the imaging system for the specimen or another specimen to a common reference.

Image alignment setup for specimens with intra- and inter-specimen variations using unsupervised learning and adaptive database generation methods

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.

Print check repeater defect detection

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Beaverton, OR, United States of America

Bjorn Brauer