Asml Netherlands B.v.

Alibaba Group Holding Limited

Jun Tao

Yen-Wen Lu

Been-Der Chen

Jiangwei Li

Min-Chun Tsai

Dong Mao

Yu Long Cao

Christopher A Spence

Jing Su

Ya Luo

Luo Si

Jun Lang

Yun Liu

Yuliang Yan

Stanislas Hugo Louis Baron

Yu Cao

Shuyu Ai

Jianhua Wen

Keyao Zhu

Xu Xie

A method for training a machine learning model to generate a characteristic pattern, the method includes obtaining training data associated with a reference feature in a reference image. The training data includes (i) location data of each portion of the reference feature, and (ii) a presence value indicating whether the portion of the reference feature is located within a reference assist feature generated for the reference feature. The method includes training the machine learning model to predict a presence value based on the actual presence value in the training data. The predicted presence value indicates whether a portion of a feature (e.g., a skeleton point on a skeleton of a contour of the feature) is to be covered by an assist feature. The training is performed based on the training data such that a metric between a predicted presence value and the presence value is minimized.

Machine learning based subresolution assist feature placement

Training methods and a mask correction method. One of the methods is for training a machine learning model configured to predict a post optical proximity correction (OPC) image for a mask. The method involves obtaining (i) a pre-OPC image associated with a design layout to be printed on a substrate, (ii) an image of one or more assist features for the mask associated with the design layout, and (iii) a reference post-OPC image of the design layout; and training the machine learning model using the pre-OPC image and the image of the one or more assist features as input such that a difference between the reference image and a predicted post-OPC image of the machine learning model is reduced.

Method for training machine learning model to determine optical proximity correction for mask

An interactive recommendation system is provided. The system comprises an information generation module configured to: provide a set of information contents for outputting in an information interface, wherein each of the set of information contents is associated with an information item; receive a selection of one of the set of information contents that signals the user's interest or disinterest in an information item; determine, based on a model and history data related to prior activities of the user including a plurality of selections from the set of information contents, one or more information items to be included in recommendation information for the user; provide the recommendation information for outputting in a recommendation information interface; and update, based on the history data, the set of information contents for outputting in the information interface.

Method and apparatus for real-time interactive recommendation

Described herein is a method for obtaining a preferred layout for a lithographic process, the method comprising: identifying an initial layout including a plurality of features; and reconfiguring the features until a termination condition is satisfied, thereby obtaining the preferred layout; wherein the reconfiguring comprises evaluating a cost function that measures how a lithographic metric is affected by a set of changes to the features for a plurality of lithographic process conditions, and expanding the cost function into a series of terms at least some of which are functions of characteristics of the features.

Method and apparatus for cost function based simultaneous OPC and SBAR optimization

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Cupertino, CA, United States of America

Jun Tao