Tissue simulating optical multilayer phantoms that closely mimic optical properties of specific tissues are used to evaluate and calibrate spectral imaging studies in clinical diagnostics. Consistency in phantom layer thickness, fabricated through the spin coating method is crucial in using the methodology to create large datasets to facilitate artificial intelligence (AI) based optical diagnostic systems. These phantoms are usually imaged under a scanning electron microscope to verify layer dimensions, varying with the amount of used materials, changes in sample parameters, and spin speed. Imaging a considerable number of phantoms under SEM for different applications is challenging. This paper addresses the automated detection of layered skin phantom thickness acquired from light microscopy images. The thickness estimation reported in this work is also useful in optimizing the sample and spin coating parameters to achieve a predefined sample dimension during the fabrication procedure. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.