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.