徐绍夫助理教授

址:上海交大闵行校区综合实验楼606

话:

箱:s.xu@sjtu.edu.cn

研究中心:智能微波光波融合创新中心

个人主页:https://imlic.sjtu.edu.cn/?p=3096

个人简介
徐绍夫,上海交通大学助理教授,博士生导师,分别于2017、2022年在上海交通大学电子工程系取得学士与博士学位,2022年加入上海交通大学。在Light: Science & Applications等光电子学重要期刊上发表学术论文20余篇,授权中国发明专利11项,美国发明专利3项。获2022年自然科学基金委青年科学基金支持,以骨干身份参与参与重点研发计划、科技委专题项目、装发预研课题等项目。
研究领域
光电信号处理
研究方向

1、微波光子宽带信号处理技术

2、光学神经网络计算加速技术

3、光学矩阵(张量)处理器及片上集成技术

获奖情况
2021年 全国微波光子学学术新星
著作及专利

[1]    邹卫文,徐绍夫,陈建平,可实现智能信号处理的高速高精度光模数转换装置和方法,CN108375861B

[2]    邹卫文,于磊,徐绍夫,马伯文,陈建平,智能决策的光子处理系统和处理方法,CN108599849B

[3]    邹卫文,徐绍夫,王静,陈建平,平铺型光子神经网络卷积层芯片,CN109254350B

[4]    邹卫文,徐绍夫,王静,陈建平,硅基铌酸锂薄膜电光调制器阵列集成的方法,CN110161625B

[5]    邹卫文,王静,徐绍夫,王兴军,基于铌酸锂-硅晶圆的高速低电压电光调制器,CN111175999B

[6]    邹卫文,邹秀婷,徐绍夫,钱娜,基于卷积循环自动编码器的并行光模数转换系统和方法,CN111650803B

[7]    邹卫文,王静,徐绍夫,一种基于多材料体系的光电单片集成系统,CN111474745B

[8]    邹卫文,徐绍夫,王静,王兴军,基于铌酸锂-硅晶圆的单片集成光模数转换系统及制备方法,CN111176053B

[9]    邹卫文,王静,徐绍夫,刘建国,基于铌酸锂-硅晶圆的光电单片集成系统,CN111273464B

[10] 邹卫文,邹秀婷,徐绍夫,陈建平,基于神经网络的宽带微波瞬时频率测量优化方法和装置,CN110133382B

[11] 邹卫文,徐绍夫,陈建平,基于微环谐振器的光子神经网络卷积层芯片,CN109639359B

[12] 邹卫文,徐绍夫,陈建平,Deep learning based method and device for noise suppression and distortion correction of analog-to-digital convertersUS10812095B2

[13] 邹卫文,徐绍夫,陈建平,High-speed and high-precision photonic analog-to-digital conversion device and method for realizing intelligent signal processing using the sameUS10651867B2

[14] 邹卫文,徐绍夫,王静,陈建平,Silicon-based lithium niobate film electro-optic modulator array and integration method thereof, US11204535B2

重要论文

[1]     Xinrui Zhao, Shaofu Xu, Sicheng Yi, Shiyu Hua, Xing Li, and Weiwen Zou, Photonic parallel channel estimation of MIMO-OFDM wireless communication systems, Optics Express 31, 1394-1408 (2023).

[2]     Shaofu Xu, Jing Wang, Sicheng Yi, and Weiwen Zou, High-order tensor flow processing using integrated photonic circuits, Nature Communications 13, 7970 (2022).

[3]     Sicheng Yi, Shaofu Xu, Jing Wang, and Weiwen Zou, Enhancement of calculation accuracy of the integrated photonic tensor flow processer by global optical power allocation, Optics Letters 47, 6409-6412 (2022).

[4]     Shaofu Xu, Jing Wang, Sicheng Yi, Xinrui Zhao, Binshui Liu, Jiayi Shao, and Weiwen Zou, Parallel optical coherent dot-product architecture for large-scale matrix multiplication with compatibility for diverse phase shifters, Optics Express 30, 42057-42068 (2022).

[5]     Yunkun Liu, Anyi Deng, Shiyu Hua, Shaofu Xu, and Weiwen Zou, Photonic ADC-based scheme for joint wireless communication and radar by adopting a broadband OFDM shared signal, Optics Letters 47, 5421-5424 (2022).

[6]     Shaofu Xu, Jing Wang, Haowen Shu, Zhike Zhang, Sicheng Yi, Bowen Bai, Xingjun Wang, Jianguo Liu, and Weiwen Zou, Optical coherent dot-product chip for sophisticated deep learning regression, Light: Science & Applications 10, 221 (2021).

[7]     Shaofu Xu, Jing Wang, and Weiwen Zou, Optical convolutional neural network with WDM-based optical patching and microring weighting banks, IEEE Photonics Technology Letters 33, 89-92 (2021).

[8]     Shaofu Xu, Jing Wang, and Weiwen Zou, Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines, Optics Letters 45, 3689-3692 (2020).

[9]     Shaofu Xu, Xiuting Zou, Bowen Ma, Jianping Chen, Lei Yu, and Weiwen Zou, Deep-learning-powered photonic analog- to-digital conversion, Light: Science & Applications 8, 66 (2019).

[10]   Shaofu Xu, Jing Wang, Rui Wang, Jianping Chen, and Weiwen Zou, High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays, Optics Express 27, 19778-19787 (2019).