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

Ziyang Wang¹, Xielin Wang^1,2, Enzi Zhai³

¹Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.

|June 11, 2026

Summary

This summary is machine-generated.

ReflectoRNN, an AI tool, accurately extracts material refractive indices from light reflection data in real-time. This advances in-operando optical characterization for dynamic materials and devices.

Keywords:

artificial intelligence complex refractive index dielectric Bragg reflector evolving materials optical reflectometry recurrent neural network

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Area of Science:

Materials Science
Optoelectronics
Artificial Intelligence

Background:

Complex refractive indices are crucial for light-matter interactions and designing photonic devices.
Real-time tracking of refractive index changes in materials under external stimuli is challenging.
Conventional methods like ellipsometry are often impractical for dynamic or multilayered systems.

Purpose of the Study:

To develop an AI-powered framework for real-time extraction of complex refractive indices from reflectance spectra.
To enable accurate in-operando optical characterization of materials undergoing external stimuli.
To overcome limitations of traditional methods in dynamic and complex material systems.

Main Methods:

Development of ReflectoRNN, an AI framework utilizing recurrent neural networks (RNN).
Application of ReflectoRNN to analyze reflectance spectra under thermal, electrical, magnetic, or mechanical stimuli.
Validation using generated datasets and experimental measurements on MoS2 and WS2 across various substrates and structures.

Main Results:

ReflectoRNN achieved high accuracy with a median Pearson's correlation coefficient (PCC) of 0.998 and a relative accuracy score (RAS) of 0.968 on generated data.
Experimental validation on MoS2 and WS2 demonstrated high accuracy and physical consistency across diverse substrates and multilayer stacks.
Temperature-dependent measurements showed resonance energy matching Bose-Einstein predictions, confirming physical validity.

Conclusions:

ReflectoRNN enables accurate, real-time in-operando extraction of complex refractive indices.
The AI framework significantly advances optical characterization for dynamic materials and complex photonic structures.
This work paves the way for automated material monitoring and accelerated materials discovery.