A Deep Learning Model for Spectral Reconstruction of Arrayed Micro-Resonators
Miniaturized spectrometers employing photonic crystal cavity arrays in conjunction with computational reconstruction have gained attention as effective tools for spectral analysis. Nevertheless, achieving an optimal balance among spectral resolution, detection range, and device compactness remains c...
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MDPI AG
2025-05-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/12/5/449 |
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| author | Xinyi Zhou Cheng Zhang Zhenyu Zheng Hongbin Li Chao Peng |
| author_facet | Xinyi Zhou Cheng Zhang Zhenyu Zheng Hongbin Li Chao Peng |
| author_sort | Xinyi Zhou |
| collection | DOAJ |
| description | Miniaturized spectrometers employing photonic crystal cavity arrays in conjunction with computational reconstruction have gained attention as effective tools for spectral analysis. Nevertheless, achieving an optimal balance among spectral resolution, detection range, and device compactness remains challenging, particularly when complex nonlinear mappings, inter-pattern correlations, and noise interference are involved. In this work, we present ESTspecNet, a deep learning framework that integrates EfficientNet, the Swin Transformer, and spatial-channel attention mechanisms to improve spectral reconstruction accuracy. We reconstructed near-infrared spectra over an 80 nm range using a 144-unit photonic crystal cavity array, and achieved a single-peak resolution of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.47</mn></mrow></semantics></math></inline-formula> nm and a double-peak resolution of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.7</mn></mrow></semantics></math></inline-formula> nm. Compared to conventional methods, the proposed model demonstrates superior performance in both wide-range spectral reconstruction and fine-resolution tasks, thus highlighting its ability to effectively capture intricate spectral features and long-range dependencies, thereby advancing the reconstruction capabilities of miniaturized spectrometers. |
| format | Article |
| id | doaj-art-464b8bfb4fa54a4e825a811584000c52 |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-464b8bfb4fa54a4e825a811584000c522025-08-20T03:48:02ZengMDPI AGPhotonics2304-67322025-05-0112544910.3390/photonics12050449A Deep Learning Model for Spectral Reconstruction of Arrayed Micro-ResonatorsXinyi Zhou0Cheng Zhang1Zhenyu Zheng2Hongbin Li3Chao Peng4State Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, ChinaState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, ChinaState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, ChinaState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, ChinaState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, ChinaMiniaturized spectrometers employing photonic crystal cavity arrays in conjunction with computational reconstruction have gained attention as effective tools for spectral analysis. Nevertheless, achieving an optimal balance among spectral resolution, detection range, and device compactness remains challenging, particularly when complex nonlinear mappings, inter-pattern correlations, and noise interference are involved. In this work, we present ESTspecNet, a deep learning framework that integrates EfficientNet, the Swin Transformer, and spatial-channel attention mechanisms to improve spectral reconstruction accuracy. We reconstructed near-infrared spectra over an 80 nm range using a 144-unit photonic crystal cavity array, and achieved a single-peak resolution of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.47</mn></mrow></semantics></math></inline-formula> nm and a double-peak resolution of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.7</mn></mrow></semantics></math></inline-formula> nm. Compared to conventional methods, the proposed model demonstrates superior performance in both wide-range spectral reconstruction and fine-resolution tasks, thus highlighting its ability to effectively capture intricate spectral features and long-range dependencies, thereby advancing the reconstruction capabilities of miniaturized spectrometers.https://www.mdpi.com/2304-6732/12/5/449miniaturized spectrometerresonant arraydeep learningspectral reconstruction |
| spellingShingle | Xinyi Zhou Cheng Zhang Zhenyu Zheng Hongbin Li Chao Peng A Deep Learning Model for Spectral Reconstruction of Arrayed Micro-Resonators Photonics miniaturized spectrometer resonant array deep learning spectral reconstruction |
| title | A Deep Learning Model for Spectral Reconstruction of Arrayed Micro-Resonators |
| title_full | A Deep Learning Model for Spectral Reconstruction of Arrayed Micro-Resonators |
| title_fullStr | A Deep Learning Model for Spectral Reconstruction of Arrayed Micro-Resonators |
| title_full_unstemmed | A Deep Learning Model for Spectral Reconstruction of Arrayed Micro-Resonators |
| title_short | A Deep Learning Model for Spectral Reconstruction of Arrayed Micro-Resonators |
| title_sort | deep learning model for spectral reconstruction of arrayed micro resonators |
| topic | miniaturized spectrometer resonant array deep learning spectral reconstruction |
| url | https://www.mdpi.com/2304-6732/12/5/449 |
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