A Neural-Assisted Triangulation Framework for Broadband Optical Monitoring of Multilayer Coatings

Broadband optical monitoring provides rich spectral information for the fabrication of multilayer optical coatings, but conventional online characterization generally relies on iterative thickness re-estimation, whose computational burden increases when information from multiple deposition stages is incorporated. To address this issue, this work proposes a neural-assisted triangulation framework that organizes multistage residual spectra within a fixed-length historical observation window, thereby maintaining a fixed-dimensional estimator input. The framework further reformulates conventional iterative online thickness re-estimation as direct neural prediction of incremental thickness-state corrections. A shared one-dimensional convolutional encoder extracts spectral features from the residual history, which are then fused with the current pre-update thickness state for thickness-state updating. The updated thickness estimates are subsequently used to reconstruct the multilayer optical model and calculate the reference spectrum for the next deposition stage. The estimator is trained offline using synthetic thickness-state perturbations and a physics-guided objective that combines direct state supervision with spectral-reconstruction and spectral-gradient constraints, while only neural inference is required during online deposition. The proposed framework was implemented on an ion-beam sputtering platform and evaluated through three consecutive fabrication runs of an 82-layer long-wave-pass filter. All three fabricated filters satisfied the specified spectral requirements, with average stopband transmittance ranging from 0.090% to 0.155% and average passband transmittance ranging from 95.008% to 95.105%. The mean cutoff wavelength was 712.461 nm, with a standard deviation of 0.278 nm and a maximum inter-run difference of 0.555 nm. These results demonstrate the practical feasibility of the proposed framework for broadband monitoring of high-layer-count optical coatings.

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Publication Details

Journal
Coatings
Published
2026-10-26
DOI
https://doi.org/10.3390/coatings16111276
Primary Topic
Optical Coatings and Gratings
Type
article
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article

A Neural-Assisted Triangulation Framework for Broadband Optical Monitoring of Multilayer Coatings

Zhuangzhuang He, Yiming Guo, Yuhui Pei, Ming Ji et al.
Coatings
Optical Coatings and Gratings
article

A Neural-Assisted Triangulation Framework for Broadband Optical Monitoring of Multilayer Coatings

Zhuangzhuang He, Yiming Guo, Yuhui Pei, Ming Ji, Xiaohan Ji, Bo Wang
article en

Abstract

Broadband optical monitoring provides rich spectral information for the fabrication of multilayer optical coatings, but conventional online characterization generally relies on iterative thickness re-estimation, whose computational burden increases when information from multiple deposition stages is incorporated. To address this issue, this work proposes a neural-assisted triangulation framework that organizes multistage residual spectra within a fixed-length historical observation window, thereby maintaining a fixed-dimensional estimator input. The framework further reformulates conventional iterative online thickness re-estimation as direct neural prediction of incremental thickness-state corrections. A shared one-dimensional convolutional encoder extracts spectral features from the residual history, which are then fused with the current pre-update thickness state for thickness-state updating. The updated thickness estimates are subsequently used to reconstruct the multilayer optical model and calculate the reference spectrum for the next deposition stage. The estimator is trained offline using synthetic thickness-state perturbations and a physics-guided objective that combines direct state supervision with spectral-reconstruction and spectral-gradient constraints, while only neural inference is required during online deposition. The proposed framework was implemented on an ion-beam sputtering platform and evaluated through three consecutive fabrication runs of an 82-layer long-wave-pass filter. All three fabricated filters satisfied the specified spectral requirements, with average stopband transmittance ranging from 0.090% to 0.155% and average passband transmittance ranging from 95.008% to 95.105%. The mean cutoff wavelength was 712.461 nm, with a standard deviation of 0.278 nm and a maximum inter-run difference of 0.555 nm. These results demonstrate the practical feasibility of the proposed framework for broadband monitoring of high-layer-count optical coatings.

CoatingsVol. 16(11)
Macau University of Science and Technology (MO), Chang'an University (CN)
Openalex Percentile: Top 28%
Optical Coatings and Gratings
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