Deep Learning Assisted On-chip Fourier Transform Spectrometer

被引：2
作者：
Xia, Lipeng [1 ,2 ,3 ]
Zhang, Aoxue [1 ,2 ,3 ]
Li, Ting [1 ,2 ,3 ]
Zou, Yi [1 ]
机构：
[1] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
[2] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China
[3] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
来源：
INTEGRATED OPTICS: DEVICES, MATERIALS, AND TECHNOLOGIES XXIV | 2020年 / 11283卷
基金：
中国国家自然科学基金;
关键词：
Silicon Photonics; Fourier Transform Spectrometer; Deep learning; SOI;
D O I：
10.1117/12.2546428
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
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页数：9
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