Affiliation:
1. Shanghai Research Center for Quantum Sciences
2. Shandong Normal University
Abstract
The wavemeter is an important instrument for spectrum analysis, widely used in spectral calibration, remote sensing, atomic physics, and high-precision metrology. However, near-infrared (NIR) wavemeters require infrared-sensitive detectors that are expensive and less sensitive compared to silicon-based visible light detectors. To circumvent these limitations, we propose an NIR speckle wavemeter based on nonlinear frequency conversion. We combine a scattering medium and the deep learning technique to invert the nonlinear mapping of the NIR wavelength and speckles in the visible wave band. With the outstanding performance of deep learning, a high-precision wavelength resolution of 1 pm is achievable in our experiment. We further demonstrate the robustness of our system and show that the recognition of power parameters and multi-spectral lines is also feasible. The proposed method offers a convenient and flexible way to measure NIR light, and it offers the possibility of cost reduction in miniaturized wavemeter systems.
Funder
National Natural Science Foundation of China
Subject
Atomic and Molecular Physics, and Optics
Cited by
2 articles.
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