Machine learning inverse problem solving for optical constants determination

Optical coatings have a wide range of applications, from precision filters for cellular imaging systems to high-reflection mirrors employed in interferometric gravitational-wave detectors. The properties of these materials can have a profound effect on their performance and therefore need to be extensively characterized. One of the main material properties of interest is their optical constants (refractive index and extinction coefficient) which can be highly dependent on the deposition method. There are two main techniques that allow the determination of optical constants: ellipsometry and reflection / transmission spectrophotometry, both of which involve an assumption of the functional dependence of material's dielectric function with wavelength. In this work, we employ machine learning based methods to solve the inverse problem of determining the thickness and optical constants of a material from reflectance and transmittance measurements only. This approach does not rely on dielectric function models for the material, provides fast performance by using pre-trained modules, and employs open-source libraries to ensure open-access for all users in the optics community.