Abstract: A method for optical proximity correction (OPC) comprises creating a semi-physical model of a mask for a current layer in an IC design layout using physical parameters of a lithography process used to create the mask, the semi-physical model specifying contours of the plurality of features of the mask. It is determined from design information whether the current layer is deformed by the one or more reference layers that overlap the current layer near the contours. Responsive to determining that the current layer is deformed by the one or more reference layers, the semi-physical model and the design information of the one or more reference layers are input into a trained machine learning algorithm to generate a contour shift prediction for the current layer, the contour shift prediction estimating a residual error of the semi-physical model. The contour shift prediction is then used for multilayer OPC correction of the current layer.

Abstract: A method for optical proximity correction (OPC) comprises creating a semi-physical model of a mask for a current layer in an IC design layout using physical parameters of a lithography process used to create the of the mask, the semi-physical model specifying contours of the plurality of features of the mask. It is determined from design information whether the current layer is deformed by the one or more reference layers that overlap the current layer near the contours. Responsive to determining that the current layer is deformed by the one or more reference layers, the semi-physical model and the design information of the one or more reference layers are input into a trained machine learning algorithm to generate a contour shift prediction for the current layer, the contour shift prediction estimating a residual error of the semi-physical model. The contour shift prediction is then used for multilayer OPC correction of the current layer.

Abstract: A method of predictive control for a single input, single output (SISO) system, including modeling the SISO system with model factors, detecting output from the SISO system, estimating a filtered disturbance from the output, determining a steady state target state from the filtered disturbance and a steady state target output, populating a dynamic optimization solution table using the model factors and a main tuning parameter, and determining an optimum input from the dynamic optimization solution table. Determining an optimum input includes determining a time varying parameter, determining a potential optimum input from the time varying parameter, and checking whether the potential optimum input is the optimum input.

Abstract: A method of predictive control for a single input, single output (SISO) system, including modeling the SISO system with model factors, detecting output from the SISO system, estimating a filtered disturbance from the output, determining a steady state target state from the filtered disturbance and a steady state target output, populating a dynamic optimization solution table using the model factors and a main tuning parameter, and determining an optimum input from the dynamic optimization solution table. Determining an optimum input includes determining a time varying parameter, determining a potential optimum input from the time varying parameter, and checking whether the potential optimum input is the optimum input.