Abstract: Reinforcement learning methods are applied to the multi-domain problem of developing photoresist models for advanced semiconductor technologies. In an iterative process, candidate photoresist models are selected or generated, with each model comprising an optical imaging model, one or more analytical chemistry or deformation kernels, and one or more photoresist development model terms. Model parameters to be calibrated in an iteration are selected. The candidate photoresist models are calibrated to best fit photoresist contours extracted from SEM images. Values for the calibration model parameters are determined and the most useful analytical kernels are kept in each model while the others are dropped. A genetic algorithm uses the best calibrated photoresist models from the prior iteration to develop candidate models for the next iteration. The process iterates until no further accuracies can be gained. A residual minimization model can be trained to correct for residual errors in the final model.