SnS₂/WSe₂ van der Waals Single-Detector Spectrometer With a Dynamically Selecting Spectral Reconstruction Strategy

The single-detector spectrometers based on 2D layer van der Waals (vdW) heterojunctions offer advantages in spectral reconstruction due to their high sensitivity, tunable optical properties, and the ability to cover a broad spectral range. There exist two principal algorithms dominating spectrum rec...

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Vydané v:IEEE electron device letters Ročník 46; číslo 5; s. 801 - 804
Hlavní autori: Zhou, Yang, Mu, Haoran, Zhang, Congwen, Xu, Renjing, Zhang, Guangyu, Lin, Shenghuang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
2D layer van der Waals heterojunctions
Accuracy
Algorithms
Artificial neural networks
Broadband communication
Classification algorithms
computational spectrometer
deep learning
Heterojunctions
hybrid spectral reconstruction algorithm
Image reconstruction
Lakes
Least squares method
Machine learning
Neural networks
Optical properties
Photoconductivity
Radio frequency
Reconstruction
Reconstruction algorithms
Regularization
Regularization methods
Spectral resolution
Spectrometers
Support vector machines
Training
ISSN:0741-3106, 1558-0563
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Shrnutí:The single-detector spectrometers based on 2D layer van der Waals (vdW) heterojunctions offer advantages in spectral reconstruction due to their high sensitivity, tunable optical properties, and the ability to cover a broad spectral range. There exist two principal algorithms dominating spectrum reconstruction for this kind spectrometer: the Tikhonov regularization method combined with the Least Squares Method (LSM) and neural network-based approaches, particularly Deep Learning (DL). However, both of the algorithms exhibit inherent limitations in spectral reconstruction, which constrain the versatility of computational spectrometers that rely solely on a single algorithm for reconstructing diverse spectral profiles. To overcome this limitation, we introduce an artificial neural network (ANN)-based classification model capable of dynamically selecting the optimal algorithm throughout the reconstruction process. This enables highly accurate spectral reconstruction within the 440-700 nm wavelength range, achieving a spectral resolution of 6 nm. By harnessing the complementary strengths of multiple algorithms, our approach proposes a novel strategy for combining techniques to enhance the precision of spectral reconstructions, laying the groundwork for more sophisticated methods in the future.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0741-3106
1558-0563
DOI:10.1109/LED.2025.3545960