![]() |
个人信息Personal Information
副教授
博士生导师
教师拼音名称:Sun Zhi
电子邮箱:zhisun@tsinghua.edu.cn
办公地点:罗姆楼9层102
联系方式:010-62770747
学位:博士学位
毕业院校:美国佐治亚理工学院
- 1. Z. Sun, H. Guo, and I. F. Akyildiz, “High-data-rate Long-range Underwater Communications via Acoustic Reconfigurable Intelligent Surfaces”, IEEE Communications Magazine, Vol. 60, No. 10, pp.96-102, October 2022.
- 1. Z. Sun, H. Guo, and I. F. Akyildiz, “High-data-rate Long-range Underwater Communications via Acoustic Reconfigurable Intelligent Surfaces”, IEEE Communications Magazine, Vol. 60, No. 10, pp.96-102, October 2022.
- 2. Z. Li and Z. Sun, "Optimal Active and Reconfigurable Meta-sphere Design for Metamaterial-enhanced Magnetic Induction Communications", IEEE Transactions on Antennas and Propagation, Vol. 70, No.9, pp.8148-8163, September 2022.
- 2. Z. Li and Z. Sun, "Optimal Active and Reconfigurable Meta-sphere Design for Metamaterial-enhanced Magnetic Induction Communications", IEEE Transactions on Antennas and Propagation, Vol. 70, No.9, pp.8148-8163, September 2022.
- 3. S. Rajendran and Z. Sun, "RF Impairment Model-Based IoT Physical-Layer Identification for Enhanced Domain Generalization", IEEE Transactions on Information Forensics and Security, Vol. 17, pp. 1285-1299, March 2022.
- 3. S. Rajendran and Z. Sun, "RF Impairment Model-Based IoT Physical-Layer Identification for Enhanced Domain Generalization", IEEE Transactions on Information Forensics and Security, Vol. 17, pp. 1285-1299, March 2022.
- 4. Z. Sun, S. Balakrishnan, L. Su, A. Bhuyan, P. Wang, and C. Qiao, "Who is in Control? Practical Physical Layer Attack and Defense for mmWave based Sensing in Autonomous Vehicles", IEEE Transactions on Information Forensics and Security, Vol.16, pp.3199-3214, May 2021.
- 4. Z. Sun, S. Balakrishnan, L. Su, A. Bhuyan, P. Wang, and C. Qiao, "Who is in Control? Practical Physical Layer Attack and Defense for mmWave based Sensing in Autonomous Vehicles", IEEE Transactions on Information Forensics and Security, Vol.16, pp.3199-3214, May 2021.
- 5. H. Guo, Z. Sun, and P. Wang, "Joint Design of Communication, Wireless Energy Transfer, and Control for Swarm Autonomous Underwater Vehicles", IEEE Transactions on Vehicular Technology, Vol.70, No.2, pp.1821-1835, February 2021.
- 5. H. Guo, Z. Sun, and P. Wang, "Joint Design of Communication, Wireless Energy Transfer, and Control for Swarm Autonomous Underwater Vehicles", IEEE Transactions on Vehicular Technology, Vol.70, No.2, pp.1821-1835, February 2021.
- 6. S. Rajendran, F. Lin, K. Ren, and Z. Sun, "Injecting Reliable Radio Frequency Fingerprints Using Metasurface for The Internet of Things", IEEE Transactions on Information Forensics and Security, Vol. 16, pp. 1896-1911, December 2020.
- 6. S. Rajendran, F. Lin, K. Ren, and Z. Sun, "Injecting Reliable Radio Frequency Fingerprints Using Metasurface for The Internet of Things", IEEE Transactions on Information Forensics and Security, Vol. 16, pp. 1896-1911, December 2020.
-
光电混合计算芯片及系统
人工智能浪潮和大模型的涌现使得当前算力供不应求,算力缺口持续扩大,而随着晶体管尺寸接近物理极限,近十年内摩尔定律已放缓甚至面临失效。利用光子代替电子作为新的计算载体逐渐被视为实现下一代高性能人工智能算力发展的关键技术方案之一。片上光学计算架构具有高并行、高速率、低功耗等特点,能够有效缓解目前主流微电子处理器在带宽、时延和工艺制程等方面的瓶颈。本课题组提出基于衍射的片上光计算架构,能够在极紧凑的面积下实现多种算子的并行计算加速,具有物理结构紧凑、无源低功耗、计算规模大的显著优势。
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。本课题组针对机器视觉链路对信息感知过程中的功耗、效率等瓶颈问题,提出利用光子智能的方式进行视觉感知,在光学信号传输的过程中进行光域信号处理,在传感器探测前完成特征信息提取功能。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。基于此研究方向孵化创业公司北京清智元视科技有限公司,该公司旨在通过“Al+光学”的双引擎引领下一代视觉模组的研发与制造,直面最终商业场景需求,逆向设计与优化视觉模组基本形态,提供多应用场景、高效能的视觉产品。清智元视具有成像全链路的设计、生产、装配能力,涵盖光学镜头、机械结构、电子电路、图像算法与软件。截止至今,清智元视已成功完成多家客户的影像产品交付与验收,技术指标处于业界领先地位。
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。
- 暂无内容
- 暂无内容