Abstract: In order to determine contour edges within a provided image, a plurality of image cells (e.g., groupings of pixels) are created within the image. For each image cell, a numerical value for each of the pixels is compared to a predetermined threshold value to determine comparison values for each pixel. A total numerical value for each image cell may then be determined utilizing the comparison values and numerical values for each pixel within each image cell. An associated contour cell (indicating present contour edges) is then determined for each image cell by comparing the total numerical value for the image cell to a contour cell index. These operations may be performed in parallel by a graphics processing unit (GPU) for each image cell, which may improve a performance of contour edge determination for the image. The stitching of contour edges may also be performed using the GPU, which may provide additional performance improvements for image contour extraction.

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.