集成硅基光子器件及系统

硅基光子集成是通往大规模片上光子集成的重要途径，本课题组在无源硅基集成器件与系统方面进行了深入研究，近期重点突破在于实现了目前世界上最长的片上集成大色散波导光栅。色散效应在很多领域有重要应用，比如长距离微波光子链路中的色散衰减效应可以用色散补偿器件来消除，在真时延相控阵天线阵列中，利用大色散器件可以获得灵活的真时延阵列，提升性能。在特殊应用方面，SWAP（尺寸、重量、功率）的限制很大，发展集成的大色散光学波导器件，为很多微波光子器件和系统降低SWAP提供了有效途径，是关键的集成光子器件。利用硅基集成大色散波导器件可以代替光纤色散，将长度降低4个数量级以上，通过采用超低损耗波导结构和侧壁法向量调制结构，实现了片上集成大色散波导光栅，色散值超过250ps/nm,最大群延时达到2440ps，带宽大于9.4nm，有望在微波光子学、光纤通信、光子智能等领域取得突破性应用。

光子智能感知

感知是一种高级的信息获取形式，通常需要从完备数据集中通过某种参数的提取来实现。以机器视觉任务为例，需要感知的信息，比如运动、轨迹、识别等，都需要先进行图像获取，再对图像进行抽象化和特征化，最后根据视觉任务的需要，提取相应的感知信息。这其中产生了大量的信息冗余，使得数据传输、处理、计算等效率降低。针对这种情况，我们提出利用光子智能的方式进行视觉感知，在光学图像或视频信号转换为电信号之前，进行光学域的信号处理，完成特征信息提取功能。这样就使得光信号转换成电信号的时候，所采集的信息不是完整图像信息，而是数据量大幅下降的特征信息，完成光子智能感知功能。该方法将大部分图像或视频的特征处理功能在光学域完成，具有超高速、低功耗、低计算量的优点，并针对不同的视觉任务具有空间、时间或时空联合特征的感知功能，为高效的感算一体智能感知系统提供了解决方案。该研究方向与华为、商汤等高科技公司进行紧密合作，具有很强的实用性。

光电混合计算及信息处理

光电混合计算，尤其是光电混合神经网络计算，将光电子技术与传统神经网络模型结合，能够发挥光(电)子技术特有的优势，有望突破传统电神经网络长延时、高功耗等技术瓶颈。首先，光子神经网络采用存算一体的结构，规避了电神经网络存在的潮汐性数据读写问题，从而提高网络的计算速率和功率效率；其次，光子神经网络连接链路损耗较低，故能进一步提升功率效率；并且相比于传统电器件，光器件具有更大的带宽和更短的响应时间，因此更适应神经网络的高速实时计算；此外，针对自动驾驶、图像处理这类前端为光传感的应用领域，运用光子神经网络能够在物理层直接处理信息，从而避免因光电转换所引入的延时、功耗、信噪比劣化等问题。

代表性期刊论文：

2021年：

1. Tingzhao Fu, Yubin Zang, Honghao Huang, Zhenmin Du, Chengyang Hu, Minghua Chen, Sigang Yang, and Hongwei Chen*, "On-chip photonic diffractive optical neural network based on a spatial domain electromagnetic propagation model," Opt. Express 29, 31924-31940 (2021)

2. Jiajie Teng, Chengyang Hu, Honghao Huang, Minghua Chen, Sigang Yang, and Hongwei Chen*, "Single-shot 3D tracking based on polarization multiplexed Fourier-phase camera," Photon. Res. 9, 1924-1930 (2021)

3. Zhenmin Du, Chengyang Hu, Tingzhao Fu, Minghua Chen, Sigang Yang, Hongwei Chen. Ptychography Based on Integrated Beam Steering Chip[J]. Chinese Journal of Lasers, 2021, 48(15): 1517004(in chinese) 

4. 陈宏伟, 杜振民, 符庭钊, 杨四刚, 陈明华. 硅基光子集成宽带大色散延时芯片（特邀）[J]. 红外与激光工程,  50(7): 20211045, 2021,.

5. Wanxin Shi, Chengyang Hu, Sigang Yang, Minghua Chen, and Hongwei Chen, "Optical random speckle encoding based on hybrid wavelength and phase modulation," Opt. Lett. 46, 3745-3748 (2021)

6. Chengyang Hu, Honghao Huang, Minghua Chen, Sigang Yang, and Hongwei Chen, "FourierCam: a camera for video spectrum acquisition in a single shot," Photon. Res. 9, 701-713 (2021)

7. Chengyang Hu, Honghao Huang, Minghua Chen, Sigang Yang, and Hongwei Chen , "Video object detection from one single image through opto-electronic neural network", APL Photonics 6, 046104 (2021)

8. Yubin Zang, Minghua Chen, Sigang Yang, Hongwei Chen. Optoelectronic convolutional neural networks based on time-stretch method. Sci. China Inf. Sci. 64, 122401 (2021)

2020年：

1. Zhao, W., Wang, H., Guo, Y. et al. A high-throughput label-free time-stretch acoustofluidic imaging cytometer for single-cell mechanotyping. Microfluid Nanofluid 24, 88 (2020)

2. H. Huang, C. Hu, S. Yang, M. Chen and H. Chen, "Temporal Ghost Imaging by Means of Fourier Spectrum Acquisition," in IEEE Photonics Journal, vol. 12, no. 5, pp. 1-12, Oct. 2020

3. Zhenmin Du, Chao Xiang, Tingzhao Fu, Minghua Chen, Sigang Yang, John E. Bowers, and Hongwei Chen , "Silicon nitride chirped spiral Bragg grating with large group delay", APL Photonics 5, 101302 (2020) Editor's Picks （主编推荐）

4. Wanyue Zhao, Yingxue Guo, Sigang Yang, Minghua Chen, Hongwei Chen, "Fast intelligent cell phenotyping for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm," J. Biomed. Opt. 25(6) 066001 (3 June 2020)

5. 陈宏伟, 于振明, 张天, 臧裕斌, 淡一航, & 徐坤. 光子神经网络发展与挑战. 中国激光, v.47;No.521(05), 80-91 (2020). （特邀综述）

6. Chengyang Hu, Zhaoyang Wu, Xusan Yang, Wanyue Zhao, Chenshuo Ma, Minghua Chen, Peng Xi, and Hongwei Chen, "MUTE-SIM: multiphoton up-conversion time-encoded structured illumination microscopy," OSA Continuum 3, 594-608 (2020)

7.  Hu, C. , Yang, S. , Chen, M. , & Chen, H. . Quadrature multiplexed structured illumination imaging. IEEE Photonics Journal, 12(2), 1-8 (2020). 

8. Jiajie Teng, Qiang Guo, Minghua Chen, Sigang Yang, and Hongwei Chen , "Time-encoded single-pixel 3D imaging", APL Photonics 5, 020801 (2020) (Invited talk)

9. Y. Zang, M. Chen, S. Yang and H. Chen, "Electro-Optical Neural Networks Based on Time-Stretch Method," in IEEE Journal of Selected Topics in Quantum Electronics, vol. 26, no. 1, pp. 1-10, Jan.-Feb. 2020

2019年之前：

1. Z. Du et al., "Integrated Wavelength Beam Emitter on Silicon for Two-Dimensional Optical Scanning," in IEEE Photonics Journal, vol. 11, no. 6, pp. 1-10, Dec. 2019

2. Chengyang Hu, Zhenmin Du, Minghua Chen, Sigang Yang, and Hongwei Chen, "Single-shot ultrafast phase retrieval photography," Opt. Lett. 44, 4419-4422 (2019)

3. Wanyue Zhao, Chao Wang, Hongwei Chen, Minghua Chen, Sigang Yang, "High-speed cell recognition algorithm for ultrafast flow cytometer imaging system," J. Biomed. Opt. 23(4) 046001 (5 April 2018)
   

4. Chen, H. Toward unlimited temporal resolution: femtosecond videography for atomic and molecular dynamics. Light: Science & Applications 6, e17123 (2017)

5. Q. Guo et al., "High-Speed Compressive Microscopy of Flowing Cells Using Sinusoidal Illumination Patterns," in IEEE Photonics Journal, vol. 9, no. 1, pp. 1-11, Feb. 2017

6. Yuxi Wang, Qiang Guo, Hongwei Chen, Minghua Chen, Sigang Yang, and Shizhong Xie, "Time-encoded structured illumination microscopy: toward ultrafast superresolution imaging," Opt. Lett. 41, 3755-3758 (2016)

1. Tingzhao Fu, Zhenmin Du, Minghua Chen, Sigang Yang, Hongwei Chen, "Off-chip far-field focusing with annular coupling gratings," Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 117636M (12 March 2021)

2. Zhenmin Du, Tingzhao Fu, Minghua Chen, Sigang Yang, Hongwei Chen, "Silicon nitride integrated two-dimensional wavelength tuning optical phased array," Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 117638O (12 March 2021)

3. W. Shi, C. Hu, S. Yang, M. Chen, and H. Chen, "High-speed Wavelength-dependent Speckle Generator Applied to Compressive Video Sensing," in Conference on Lasers and Electro-Optics, OSA Technical Digest (Optical Society of America, 2021), paper JTu3A.172.

4. Y. Zang, Z. Yu, K. Xu, M. Chen, S. Yang and H. Chen, "Universal Fiber Models based on PINN Neural Network," 2020 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC), 2020, pp. 1-3. 

5. J. Teng, Q. Guo, Y. Wang, S. Yang, M. Chen, and H. Chen, "3D surface profile imaging based on time-encoded structured illumination," in Conference on Lasers and Electro-Optics, OSA Technical Digest (Optical Society of America, 2019), paper ATh1K.2.

6. Y. Zang, H. Chen, S. Yang, and M. Chen, "High Accuracy Non Ambiguity ToF Lidar System based on Pseudo-Random Noise Code and Phase Detection Method," in Optical Fiber Communication Conference (OFC) 2019, OSA Technical Digest (Optical Society of America, 2019), paper W2A.43.