Asml Netherlands B.v.

Intel Corporation

Teng Wang

Wei Fang

Lingling Pu

Wentian Zhou

Ming Xu

Liangjiang Yu

Nan Zhao

Stephen S Chang

Nishit Patel

Sreedhar Ravipalli

Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.

Fully automated SEM sampling system for e-beam image enhancement

Systems and methods for optimal electron beam metrology guidance are disclosed. According to certain embodiments, the method may include receiving an acquired image of a sample, determining a set of image parameters based on an analysis of the acquired image, determining a set of model parameters based on the set of image parameters, generating a set of simulated images based on the set of model parameters. The method may further comprise performing measurement of critical dimensions on the set of simulated images and comparing critical dimension measurements with the set of model parameters to provide a set of guidance parameters based on comparison of information from the set of simulated images and the set of model parameters. The method may further comprise receiving auxiliary information associated with target parameters including critical dimension uniformity.

Systems and methods of optimal metrology guidance

In connection with an access of content from a cache, a snoop request can be sent to one or more remote cache devices to determine if any other cache has a copy of the content. A link between the cache and the remote cache devices can include a snoop bypass device. The snoop bypass device can monitor content cached by the one or more remote devices on a cache line or coarser granularity. The snoop bypass device can respond to the snoop request with a negative indication based on a coarser granularity tracking of content of the one or more remote cache devices.

Management of cache use requests sent to remote cache devices

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San Jose, CA, United States of America

Teng Wang