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Proceedings Paper

Deep-learning-assisted on-chip Fourier transform spectrometer
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Paper 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.

Paper Details

Date Published: 25 February 2020
PDF: 9 pages
Proc. SPIE 11283, Integrated Optics: Devices, Materials, and Technologies XXIV, 1128305 (25 February 2020); doi: 10.1117/12.2546428
Show Author Affiliations
Lipeng Xia, ShanghaiTech Univ. (China)
Univ. of Chinese Academy of Sciences (China)
Aoxue Zhang, ShanghaiTech Univ. (China)
Univ. of Chinese Academy of Sciences (China)
Ting Li, ShanghaiTech Univ. (China)
Univ. of Chinese Academy of Sciences (China)
Yi Zou, ShanghaiTech Univ. (China)


Published in SPIE Proceedings Vol. 11283:
Integrated Optics: Devices, Materials, and Technologies XXIV
Sonia M. García-Blanco; Pavel Cheben, Editor(s)

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