Machine Learning Enabled Material Identification For Uncooled Multispectral Microbolometers

This study explores machine learning for microbolometer design, evaluation, and selection. A neural net predicts material mixtures for a single pixel using a simulation that predicts multispectral responses relative to a set of basis functions. The study aims to automate AI/ML design and search.