Co-optimization of optical and resist models in the OPC modeling process using in-house genetic algorithm

Traditional modeling of computational lithography starts first by determining the functional relationship between the change in focus and the aerial image (AI) location of the optical model by setting constraints and then calibrating the resist model separately. In this process, built-in genetic algorithm (GA) tools usually participate in the parameter optimization process of only one model at a time. Additionally, GA tools are vulnerable to becoming trapped in a locally optimal solution. The practice of optimizing the optical and resist models separately may potentially miss better solutions. We propose a method to co-optimize the two models simultaneously. This is done by finding the Pareto optimal frontier of potentially better solution candidates that balance these two models. To avoid the local optimal solution trap, a method is proposed to increase the search range when the algorithm is confined. In the selecting and scoring models process, we quantify metrics that are typically made empirically by engineers to achieve higher levels of automation.