Deep-learning-assisted on-chip Fourier transform spectrometer

Lipeng Xia; Aoxue Zhang; Ting Li; Yi Zou

doi:10.1117/12.2546428

25 February 2020 Deep-learning-assisted on-chip Fourier transform spectrometer

Lipeng Xia, Aoxue Zhang, Ting Li, Yi Zou

Proceedings Volume 11283, Integrated Optics: Devices, Materials, and Technologies XXIV; 1128305 (2020) https://doi.org/10.1117/12.2546428
Event: SPIE OPTO, 2020, San Francisco, California, United States

Abstract

We proposed and demonstrated a deep learning assisted on-chip Fourier transform spectroscopy (FTS), using an artificial neural networks (ANN) to analyze the output stationary interferogram. It is found that, compared with the conventional FTS, the resolution could be improved without increasing the maximum path length difference and the number of MZIs, thus reducing the burden of adding more power budget. This new concept of enhancing spectral resolution may hold great promise for potential applications in integrated FTS.

Citation Download Citation

Lipeng Xia, Aoxue Zhang, Ting Li, and Yi Zou "Deep-learning-assisted on-chip Fourier transform spectrometer", Proc. SPIE 11283, Integrated Optics: Devices, Materials, and Technologies XXIV, 1128305 (25 February 2020); https://doi.org/10.1117/12.2546428

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