Deep-learning-powered photonic analogto-digital conversion

作     者：Shaofu Xu Xiuting Zou Bowen Ma Jianping Chen Lei Yu Weiwen Zou 

作者机构：State Key Laboratory of Advanced Optical Communication Systems and NetworksIntelligent Microwave Lightwave Integration Innovation Center(iMLic)Department of Electronic EngineeringShanghai Jiao Tong University200240 ShanghaiChina 

出 版 物：《Light(Science & Applications)》 (光（科学与应用)（英文版）)

年 卷 期：2019年第8卷第1期

页      面：611-621页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China(grant nos 61822508,61571292,and 61535006) the Shanghai Municipal Science and Technology Major Project(2017SHZDZX03) 

主　　题：thereby Deep overcome 

摘      要：Analog-to-digital converters(ADCs)must be high speed,broadband,and accurate for the development of modern information systems,such as radar,imaging,and communications systems;photonic technologies are regarded as promising technologies for realizing these advanced ***,we present a deep-learning-powered photonic ADC architecture that simultaneously exploits the advantages of electronics and photonics and overcomes the bottlenecks of the two technologies,thereby overcoming the ADC tradeoff among speed,bandwidth,and *** supervised training,the adopted deep neural networks learn the patterns of photonic system defects and recover the distorted data,thereby maintaining the high quality of the electronic quantized data succinctly and *** numerical and experimental results demonstrate that the proposed architecture outperforms state-ofthe-art ADCs with developable high throughput;hence,deep learning performs well in photonic ADC *** anticipate that the proposed architecture will inspire future high-performance photonic ADC design and provide opportunities for substantial performance enhancement for the next-generation information systems.