Abstract: A method for training a prediction model to generate a high-resolution image representing defects on a substrate from a low-resolution image of the substrate. The method includes inputting a first image and a reference image of defects on a substrate, which are representative of images captured using different image capture conditions, to a neural network. The neural network is executed to generate a predicted image in response to the first image. A loss function that is indicative of a difference between a defect distribution in the predicted image and a defect distribution in the reference image is calculated and the neural network is modified based on the loss function. The neural network may be trained until the loss function is minimized.

Abstract: 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.

Abstract: 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.

Abstract: Described herein is a method of determining assist features for a mask pattern. The method includes obtaining (i) a target pattern comprising a plurality of target features, wherein each of the plurality of target features comprises a plurality of target edges, and (ii) a trained sequence-to-sequence machine leaning (ML) model (e.g., long short term memory, Gated Recurrent Units, etc.) configured to determine sub-resolution assist features (SRAFs) for the target pattern. For a target edge of the plurality of target edges, geometric information (e.g., length, width, distances between features, etc.) of a subset of target features surrounding the target edge is determined. Using the geometric information as input, the ML model generates SRAFs to be placed around the target edge.

Abstract: A method of controlling an imaging process uses a qualified optical proximity correction (OPC) model, the process including obtaining an OPC model that is configured to model the behavior of OPC modifications to a pre-OPC design in a process for forming a pattern on a substrate using a post-OPC design in a patterning process, using the patterning process in a manufacturing environment, collecting process control data in substrates patterned using the patterning process in the manufacturing environment, storing the collected process control data in a database, analyzing, by a hardware computer system, the stored, collected process control data to verify that the OPC model is correcting pattern features within a selected threshold, and for pattern features falling outside the selected threshold, determining a modification to the imaging process to correct imaging errors.

Abstract: A method for determining a mask pattern and a method for training a machine learning model. The method for generating data for a mask pattern associated with a patterning process includes obtaining (i) a first mask image (e.g., CTM) associated with a design pattern, (ii) a contour (e.g., a resist contour) based on the first mask image, (iii) a reference contour (e.g., an ideal resist contour) based on the design pattern; and (iv) a contour difference between the contour and the reference contour. The contour difference and the first mask image are inputted to a model to generate mask image modification data. Based on the first mask image and the mask image modification data, a second mask image is generated for determining a mask pattern to be employed in the patterning process.

Abstract: 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.

Abstract: A method of controlling an imaging process uses a qualified optical proximity correction (OPC) model, the process including obtaining an OPC model that is configured to model the behavior of OPC modifications to a pre-OPC design in a process for forming a pattern on a substrate using a post-OPC design in a patterning process, using the patterning process in a manufacturing environment, collecting process control data in substrates patterned using the patterning process in the manufacturing environment, storing the collected process control data in a database, analyzing, by a hardware computer system, the stored, collected process control data to verify that the OPC model is correcting pattern features within a selected threshold, and for pattern features falling outside the selected threshold, determining a modification to the imaging process to correct imaging errors.

Abstract: A method includes obtaining a sub-layout having an area that is a performance limiting spot, adjusting colors of patterns in the area, and determining whether the area is still a performance limiting spot. Another method includes decomposing patterns in a design layout into multiple sub-layouts; determining for at least one area in one of the sub-layouts, the likelihood of that a figure of merit is beyond its allowed range; and if the likelihood is above a threshold, that one sub-layout has a performance limiting spot. Another method includes: obtaining a design layout having a first group of patterns and a second group of patterns, wherein colors of the first group of patterns are not allowed to change and colors of the second group of patterns are allowed to change; and co-optimizing at least the first group of patterns, the second group of patterns and an illumination of a lithographic apparatus.