Abstract: A method for determining a deformation of a resist in a patterning process. The method involves obtaining a resist deformation model of a resist having a pattern, the resist deformation model configured to simulate a fluid flow of the resist due to capillary forces acting on a contour of at least one feature of the pattern; and determining, via the resist deformation model, a deformation of a resist pattern to be developed based on an input pattern to the resist deformation model.

Abstract: A method and apparatus for analyzing an input electron microscope image of a first area on a first wafer are disclosed. The method comprises obtaining a plurality of mode images from the input electron microscope image corresponding to a plurality of interpretable modes. The method further comprises evaluating the plurality of mode images, and determining, based on evaluation results, contributions from the plurality of interpretable modes to the input electron microscope image. The method also comprises predicting one or more characteristics in the first area on the first wafer based on the determined contributions. In some embodiments, a method and apparatus for performing an automatic root cause analysis based on an input electron microscope image of a wafer are also disclosed.

Abstract: A system, method, and apparatus for determining three-dimensional (3D) information of a structure of a patterned substrate. The 3D information can be determined using one or more models configured to generate 3D information (e.g., depth information) using only a single image of a patterned substrate. In a method, the model is trained by obtaining a pair of stereo images of a structure of a patterned substrate. The model generates, using a first image of the pair of stereo images as input, disparity data between the first image and a second image, the disparity data being indicative of depth information associated with the first image. The disparity data is combined with the second image to generate a reconstructed image corresponding to the first image. Further, one or more model parameters are adjusted based on the disparity data, the reconstructed image, and the first image.

Abstract: A method involving obtaining a resist deformation model for simulating a deformation process of a pattern in resist, the resist deformation model being a fluid dynamics model configured to simulate an intrafluid force acting on the resist, performing, using the resist deformation model, a computer simulation of the deformation process to obtain a deformation of the developed resist pattern for an input pattern to the resist deformation model, and producing electronic data representing the deformation of the developed resist pattern for the input pattern.

Abstract: A method for training a machine learning model includes obtaining a set of unpaired after-development (AD) images and after-etch (AE) images associated with a substrate. Each AD image in the set is obtained at a location on the substrate that is different from the location at which any of the AE images is obtained. The method further includes training the machine learning model to generate a predicted AE image based on the AD images and the AE images, wherein the predicted AE image corresponds to a location from which an input AD image of the AD images is obtained.

Abstract: A method for determining a deformation of a resist in a patterning process. The method involves obtaining a resist deformation model of a resist having a pattern, the resist deformation model configured to simulate a fluid flow of the resist due to capillary forces acting on a contour of at least one feature of the pattern; and determining, via the resist deformation model, a deformation of a resist pattern to be developed based on an input pattern to the resist deformation model.

Abstract: A method for determining a deformation of a resist in a patterning process. The method involves obtaining a resist deformation model of a resist having a pattern, the resist deformation model configured to simulate a fluid flow of the resist due to capillary forces acting on a contour of at least one feature of the pattern; and determining, via the resist deformation model, a deformation of a resist pattern to be developed based on an input pattern to the resist deformation model.

Abstract: A method involving obtaining a resist deformation model for simulating a deformation process of a pattern in resist, the resist deformation model being a fluid dynamics model configured to simulate an intrafluid force acting on the resist, performing, using the resist deformation model, a computer simulation of the deformation process to obtain a deformation of the developed resist pattern for an input pattern to the resist deformation model, and producing electronic data representing the deformation of the developed resist pattern for the input pattern.

Abstract: A method involving obtaining a resist deformation model for simulating a deformation process of a pattern in resist, the resist deformation model being a fluid dynamics model configured to simulate an intrafluid force acting on the resist, performing, using the resist deformation model, a computer simulation of the deformation process to obtain a deformation of the developed resist pattern for an input pattern to the resist deformation model, and producing electronic data representing the deformation of the developed resist pattern for the input pattern.

Abstract: Described herein is a method for training a machine learning model to determine a source of error contribution to multiple features of a pattern printed on a substrate. The method includes obtaining training data having multiple datasets, wherein each dataset has error contribution values representative of an error contribution from one of multiple sources to the features, and wherein each dataset is associated with an actual classification that identifies a source of the error contribution of the corresponding dataset; and training, based on the training data, a machine learning model to predict a classification of a reference dataset of the datasets such that a cost function that determines a difference between the predicted classification and the actual classification of the reference dataset is reduced.

Abstract: A system and method for generating predictive images for wafer inspection using machine learning are provided. Some embodiments of the system and method include acquiring the wafer after a photoresist applied to the wafer has been developed; imaging a portion of a segment of the developed wafer; acquiring the wafer after the wafer has been etched; imaging the segment of the etched wafer; training a machine learning model using the imaged portion of the developed wafer and the imaged segment of the etched wafer; and applying the trained machine learning model using the imaged segment of the etched wafer to generate predictive images of a developed wafer. Some embodiments include imaging a segment of the developed wafer; imaging a portion of the segment of the etched wafer; training a machine learning model; and applying the trained machine learning model to generate predictive after-etch images of the developed wafer.

Abstract: A method for determining a deformation of a resist in a patterning process. The method involves obtaining a resist deformation model of a resist having a pattern, the resist deformation model configured to simulate a fluid flow of the resist due to capillary forces acting on a contour of at least one feature of the pattern; and determining, via the resist deformation model, a deformation of a resist pattern to be developed based on an input pattern to the resist deformation model.

Abstract: A method involving obtaining a resist deformation model for simulating a deformation process of a pattern in resist, the resist deformation model being a fluid dynamics model configured to simulate an intrafluid force acting on the resist, performing, using the resist deformation model, a computer simulation of the deformation process to obtain a deformation of the developed resist pattern for an input pattern to the resist deformation model, and producing electronic data representing the deformation of the developed resist pattern for the input pattern.