ReflectoRNN: AI-Enabled In-Operando Optical Reflectometry for Evolving Materials Using a Recurrent Neural Network.

Abstract

Complex refractive indices of materials encode fundamental information on light-matter interactions and are critical for the design of advanced photonic and optoelectronic devices. In many emerging materials, refractive indices change under external stimuli such as temperature, electric fields, or strain. Tracking these changes in-operando is critical for active photonic and optoelectronic device design, but remains challenging. Conventional methods such as ellipsometry rely on labor-intensive model fitting and are often impractical for multilayer stacks or in-operando measurements. Optical reflectometry offers a simpler alternative but suffers from ambiguous extraction of refractive index from reflectance spectra and limited applicability under dynamic modulation. Here, we present <i>ReflectoRNN</i>, an artificial intelligence (AI)-powered reflectometry framework based on recurrent neural networks (RNN), for real-time extraction of complex refractive indices in evolving materials. <i>ReflectoRNN</i> extracts refractive indices from reflectance spectra under thermal, electrical, magnetic, or mechanical stimuli. It achieves a median Pearson's correlation coefficient (PCC) of 0.998 and a relative accuracy score (RAS) of 0.968 on generated datasets. Validation experiments on MoS₂ and WS₂ across diverse substrates, including single-layer and multilayer dielectric stacks, and distributed Bragg reflectors (DBRs), demonstrate high accuracy and physical consistency, with temperature-dependent exciton resonance energy matching Bose-Einstein predictions. <i>ReflectoRNN</i> enables in-operando optical characterization of materials across complex photonic structures and offers a pathway toward automated, real-time monitoring and accelerated materials discovery.