Open AccessPhotonic analog-to-digital converter powered by a generalized and robust convolutional recurrent autoencoder
Author(s) -
Xiuting Zou,
Shaofu Xu,
Anyi Deng,
Na Qian,
Rui Wang,
Weiwen Zou
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.413897
Subject(s) - dbc , spurious free dynamic range , photonics , optics , dynamic range , autoencoder , computer science , physics , electronic engineering , engineering , artificial intelligence , phase noise , artificial neural network
We propose a convolutional recurrent autoencoder (CRAE) to compensate for time mismatches in a photonic analog-to-digital converter (PADC). In contrast of other neural networks, the proposed CRAE is generalized to untrained mismatches and untrained category of signals while remaining robust to system states. We train the CRAE using mismatched linear frequency modulated (LFM) signals with mismatches of 35 ps and 57 ps under one system state. It can effectively compensate for mismatches of both LFM and Costas frequency modulated signals with mismatches ranging from 35 ps to 137 ps under another system state. When the spur-free dynamic range (SFDR) of the unpowered PADC decreases from 10.2 dBc to -3.0 dBc, the SFDR of the CRAE-powered PADC is over 31.6 dBc.