Multispectral Microscopic Multiplexed (3M) Imaging of Atomically‐Thin Crystals Using Deep Learning

Abstract Ultra‐thin atomic crystals are promising for fabricating next‐generation photonic and optoelectronic devices. Wafer‐scale characterization techniques are highly desired for efficient and accurate thickness identification of these crystals and their heterostructures. Optical contrast between atomic crystals and substrates based on Fresnel theory is a key technique for the identification of thicknesses. Both RGB color information and spectroscopic information have been explored for layer number counting and implemented with machine learning algorithms based on large amounts of data for feature extraction. In this work, a multispectral microscopic method combining the hardware design and deep‐learning algorithms is developed. Multispectral image restoration during large‐area scanning caused by optical imaging modality drifts and automated layer number identification based on multispectral grayscale images are studied using deep learning models: generative adversarial network (GAN) and 3D U‐Net. These models are trained using custom‐built multispectral data sets and evaluated quantitatively with indicators (Dice coefficient, confusion matrix, structural similarity). After these models are trained and tested, they are integrated into a graphic user interface for on‐site identification use. The developed method provides a framework using multispectral images for 3D data reconstruction and segmentation and can be implemented for wafer‐scale characterization of heterostructures containing different species of ultra‐thin atomic crystals.