Kla Tencor Corporation

Kla Corporation

Vijay Ramachandran

Bjorn Brauer

Martin Plihal

Richard Wallingford

Scott Allen Young

Saravanan Paramasivam

Prasanti Uppaluri

Erfan Soltanmohammadi

Ankit Jain

Sairam Ravu

Sophie Salomon

David Kucher

Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem configured for determining a global texture characteristic of an image of a specimen and one or more local characteristics of a localized area in the image. The system also includes one or more components executed by the computer subsystem. The component(s) include a machine learning model configured for determining information for the specimen based on the global texture characteristic and the one or more local characteristics. The computer subsystem is also configured for generating results including the determined information. The methods and systems may be used for metrology (in which the determined information includes one or more characteristics of a structure formed on the specimen) or inspection (in which the determined information includes a classification of a defect detected on the specimen).

Machine learning using a global texture characteristic for semiconductor-based applications

Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.

Diagnostic methods for the classifiers and the defects captured by optical tools

Systems and methods for providing an augmented input data to a convolutional neural network (CNN) are disclosed. Wafer images are received at a processor. The wafer image is divided into a plurality of references images each associated with a die in the wafer image. Test images are received. A plurality of difference images are created by differences the test images with the reference images. The reference images and difference images are assembled into the augmented input data for the CNN and provided to the CNN.

Data augmentation for convolutional neural network-based defect inspection

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Sunnyvale, CA, United States of America

Vijay Ramachandran