Optimizing SEM parameters for segmentation with AI – Part 2: Designing and training a regression model

Abstract

Selecting the best microscope parameters for optimal image quality currently relies on microscopists; there exist no procedures or guidelines for tuning parameters to ensure the desired image quality is achieved. More importantly, for quantitative analysis purposes, adequate image quality for segmentation should be prioritized. This paper is the second of two parts, describing a regression model, mixed input, multiple output with Keras TensorFlow, trained to predict the beam energy and probe current, two important parameters for image quality. Specifically, parameters are predicted to optimize the image quality for segmentation, using a generated training set, as described in Part 1 of this paper. Model performance is then tested on models trained with multiple different training sets, and with different proportions of simulated and acquired data. First, to examine the impact of the training set on the prediction accuracy and then, to evaluate the importance of including real data during training. The model successfully predicted the beam energy and probe current to set on the microscope to improve image quality for segmentation. Models trained with both simulated and acquired data performed the best, as evaluated by their efficacy at improving the image quality for feature segmentation.