Abstract: A method for monitoring performance of a manufacturing process is described. The method includes receiving one or more input signals that convey information related to geometry of a substrate generated by the manufacturing process; and determining, with a prediction model, variation in the manufacturing process based on the one or more input signals. A method for predicting substrate geometry associated with a manufacturing process is also described. The method includes receiving input information including geometry information and manufacturing process information for a substrate; and predicting, using a machine learning prediction model, output substrate geometry based on the input information. The method may further include tuning the predicted output substrate geometry.

Abstract: A method for determining a stack configuration for a substrate subjected to a patterning process. The method includes obtaining (i) measurement data of a stack configuration with location information on a printed substrate, (ii) a substrate model configured to predict a stack characteristic based on a location of the substrate, and (iii) a stack map including a plurality of stack configurations based on the substrate model. The method iteratively determines values of model parameters of the substrate model based on a fitting between the measurement data and the plurality of stack configurations of the stack map, and predicts an optimum stack configuration at a particular location based on the substrate model using the values of the model parameters.

Abstract: A method for determining a stack configuration for a substrate subjected to a patterning process. The method includes obtaining (i) measurement data of a stack configuration with location information on a printed substrate, (ii) a substrate model configured to predict a stack characteristic based on a location of the substrate, and (iii) a stack map including a plurality of stack configurations based on the substrate model. The method iteratively determines values of model parameters of the substrate model based on a fitting between the measurement data and the plurality of stack configurations of the stack map, and predicts an optimum stack configuration at a particular location based on the substrate model using the values of the model parameters.