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

Ziyang Wang, Xielin Wang, Enzi Zhai, Wenjing Wu, Fevronia T Andreou, Yuxuan Cosmi Lin, Shengxi Huang

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Article in ACS nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Ziyang WangDepartment of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.ORCID 0000-0003-1368-4539

Xielin WangDepartment of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.

Enzi ZhaiDepartment of Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, United States.

Wenjing WuDepartment of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.ORCID 0000-0003-0643-7986

Fevronia T AndreouDepartment of Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, United States.

Yuxuan Cosmi LinDepartment of Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, United States.ORCID 0000-0003-0638-2620

Shengxi HuangDepartment of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.ORCID 0000-0002-3618-9074

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

artificial intelligencecomplex refractive indexdielectric Bragg reflectorevolving materialsoptical reflectometryrecurrent neural network

Identifiers

PMID42275603

DOI10.1021/acsnano.6c04315

PMCPMC13325849

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LicenceCC BY-NC-ND

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