Nonlinear memristive computational spectrometer
Abstract In the domain of spectroscopy, miniaturization efforts often face significant challenges, particularly in achieving high spectral resolution and precise construction. Here, we introduce a computational spectrometer powered by a nonlinear photonic memristor with a WSe2 homojunction. This app...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-01-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-024-01703-y |
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| _version_ | 1832594427846066176 |
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| author | Xin Li Jie Wang Feilong Yu Jin Chen Xiaoshuang Chen Wei Lu Guanhai Li |
| author_facet | Xin Li Jie Wang Feilong Yu Jin Chen Xiaoshuang Chen Wei Lu Guanhai Li |
| author_sort | Xin Li |
| collection | DOAJ |
| description | Abstract In the domain of spectroscopy, miniaturization efforts often face significant challenges, particularly in achieving high spectral resolution and precise construction. Here, we introduce a computational spectrometer powered by a nonlinear photonic memristor with a WSe2 homojunction. This approach overcomes traditional limitations, such as constrained Fermi level tunability, persistent dark current, and limited photoresponse dimensionality through dynamic energy band modulation driven by palladium (Pd) ion migration. The critical role of Pd ion migration is thoroughly supported by first-principles calculations, numerical simulations, and experimental verification, demonstrating its effectiveness in enhancing device performance. Additionally, we integrate this dynamic modulation with a specialized nonlinear neural network tailored to address the memristor’s inherent nonlinear photoresponse. This combination enables our spectrometer to achieve an exceptional peak wavelength accuracy of 0.18 nm and a spectral resolution of 2 nm within the 630–640 nm range. This development marks a significant advancement in the creation of compact, high-efficiency spectroscopic instruments and offers a versatile platform for applications across diverse material systems. |
| format | Article |
| id | doaj-art-9ebd57d9dd2744d4b03b0e2d7f50ead4 |
| institution | Kabale University |
| issn | 2047-7538 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Light: Science & Applications |
| spelling | doaj-art-9ebd57d9dd2744d4b03b0e2d7f50ead42025-01-19T12:39:14ZengNature Publishing GroupLight: Science & Applications2047-75382025-01-0114111010.1038/s41377-024-01703-yNonlinear memristive computational spectrometerXin Li0Jie Wang1Feilong Yu2Jin Chen3Xiaoshuang Chen4Wei Lu5Guanhai Li6State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of SciencesState Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of SciencesState Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of SciencesState Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of SciencesState Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of SciencesState Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of SciencesState Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of SciencesAbstract In the domain of spectroscopy, miniaturization efforts often face significant challenges, particularly in achieving high spectral resolution and precise construction. Here, we introduce a computational spectrometer powered by a nonlinear photonic memristor with a WSe2 homojunction. This approach overcomes traditional limitations, such as constrained Fermi level tunability, persistent dark current, and limited photoresponse dimensionality through dynamic energy band modulation driven by palladium (Pd) ion migration. The critical role of Pd ion migration is thoroughly supported by first-principles calculations, numerical simulations, and experimental verification, demonstrating its effectiveness in enhancing device performance. Additionally, we integrate this dynamic modulation with a specialized nonlinear neural network tailored to address the memristor’s inherent nonlinear photoresponse. This combination enables our spectrometer to achieve an exceptional peak wavelength accuracy of 0.18 nm and a spectral resolution of 2 nm within the 630–640 nm range. This development marks a significant advancement in the creation of compact, high-efficiency spectroscopic instruments and offers a versatile platform for applications across diverse material systems.https://doi.org/10.1038/s41377-024-01703-y |
| spellingShingle | Xin Li Jie Wang Feilong Yu Jin Chen Xiaoshuang Chen Wei Lu Guanhai Li Nonlinear memristive computational spectrometer Light: Science & Applications |
| title | Nonlinear memristive computational spectrometer |
| title_full | Nonlinear memristive computational spectrometer |
| title_fullStr | Nonlinear memristive computational spectrometer |
| title_full_unstemmed | Nonlinear memristive computational spectrometer |
| title_short | Nonlinear memristive computational spectrometer |
| title_sort | nonlinear memristive computational spectrometer |
| url | https://doi.org/10.1038/s41377-024-01703-y |
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