Jianshi Tang Associate Professor

Phone：+86-10-62784074

Email：jtang@tsinghua.edu.cn

Web：http://stor.ime.tsinghua.edu.cn/

Address：Rm C314, School of Integrated Circuits, Tsinghua University, Beijing, China, 100084

Prof. Jianshi Tang is currently an Associate Professor in the School of Integrated Circuits at Tsinghua University. Prof. Tang received his BS degree from the Department of Microelectronics and Nanoelectronics at Tsinghua University in 2008, and his PhD degree in Electrical Engineering from University of California, Los Angeles (UCLA) in 2014. From 2015 to 2019, he worked at IBM T. J. Watson Research Center, after which he joined Tsinghua University in 2019. He received many awards including Tsinghua University Excellent Young Faculty Award, MIT TR35 China, CIE Natural Science Award, NT18 “Best Young Scientist Award”, IEEE Brain Best Paper Award, IBM Invention Achievement Award. His main research areas include emerging memory and neuromorphic computing, and monolithic 3D heterogeneous integration, etc. He has been PI/co-PI of several research projects funded by NSFC, MOST, BNRist, and Tencent. Over the past 10 years, Dr. Tang has published more than 160 articles and proceedings in top journals and international conferences, such as Nature, Science, Nature Nanotechnology, Nature Electronics, Nature Materials, IEDM, VLSI, ISSCC, etc. His work has been cited over 10000 times, and been included in the list of “Best Papers in Beijing Area”. He has also filed more than 180 patent applications, and have been granted over 60 patents. Prof. Tang is an Editor for IEEE Transactions on Electron Devices, Associate Editor for Frontiers and serves on the Editorial Board of Journal of Semiconductors and Electronics. He is an IEEE senior member, and served as Technical/Executive Committee Member for several international conference, including IEDM, IEEE-NANO, EDTM, CSTIC, etc.

Group Openings: Our group has openings for 2-4 PhD/Master students every year and regularly recruit postdocs with related background in materials, devices, nanofabrications and chips. Undergraduate students are also encouraged to participate in our research. For more information, please email jtang@tsinghua.edu.cn with your CV.

Representative Publications (as correspondence/first author):

[1] H. Zhao, Z. Liu, J. Tang^*, B. Gao, Q. Qin, J. Li, Y. Zhou, P. Yao, Y. Xi, Y. Lin, H. Qian, H. Wu, “Energy-Efficient High-Fidelity Image Reconstruction with Memristor Arrays for Medical Diagnosis”, Nature Communications, 14, 2276 (2023).

[2] Y. Du, J. Tang^*, Y. Li, Y. Xi, B. Gao, H. Qian, H. Wu, “Monolithic 3D Integration of FeFET, Hybrid CMOS Logic and Analog RRAM Array for Energy-Efficient Reconfigurable Computing-In-Memory Architecture”, VLSI, T15-4 (2023).

[3] X. Li, Y. Zhong, H. Chen, J. Tang^*, X. Zheng, W. Sun, Y. Li, D. Wu, B. Gao, X. Hu^*, H. Qian, H. Wu^*, “A Memristors-based Dendritic Neuron for High-Efficiency Spatial-Temporal Information Processing”, Advanced Materials, 2203684 (2023).

[4] Y. Zhong, J. Tang^*, X. Li, X. Liang, Z. Liu, Y. Li, Y. Xi., P. Yao, Z. Hao, B. Gao, H. Qian, H. Wu^*, “A Memristor-based Analogue Reservoir Computing System for Real-Time and Power-Efficient Signal Processing”, Nature Electronics, 5, 672-681 (2022). (Selected as Editorials)

[5] X. Liang^#, Y. Zhong^#, J. Tang^*, Z. Liu, P. Yao, K. Sun, Q. Zhang, B. Gao, H. Heidari^*, H. Qian, H. Wu^*, “Rotating Neurons for All-Analog Implementation of Cyclic Reservoir Computing”, Nature Communications, 13, 1549 (2022). (Featured in Nature Communications Editors’ Highlights)

[6] R. An^#, Y. Li^#, J. Tang^*, B. Gao, Y. Du, J. Yao, Y. Li, S. Wen, H. Zhao, J. Li, Q. Qin, Q. Zhang, S. Qiu, Q. Li, Z. Li^*, H. Qian, H. Wu^*, “A Hybrid Computing-In-Memory Architecture by Monolithic 3D Integration of BEOL CNT/IGZO-based CFET Logic and Analog RRAM”, IEDM Tech. Dig., 18.1.1-18.1.4 (2022).

[7] X. Mou, J. Tang^*, Y. Lyu, Q. Zhang, S. Yang, F. Xu, W. Liu, M. Xu, Y. Zhou, W. Sun, Y. Zhong, B. Gao, P. Yu^*, H. Qian, H. Wu, “Analog Memristive Synapse based on Topotactic Phase Transition for High-Performance Neuromorphic Computing and Neural Network Pruning”, Science Advances, 7, abh0648 (2021).

[8] Y. Zhong, J. Tang^*, X. Li, B. Gao, H. Qian, H. Wu^*, “Dynamic Memristor-based Reservoir Computing for High-Efficiency Temporal Signal Processing”, Nature Communications, 12, 408 (2021).

[9] Y. Li, J. Tang^*, B. Gao, J. Yao, Y. Xi, Y. Li, T. Li, Y. Zhou, Z. Liu, Q. Zhang, S. Qiu, Q. Li, H. Qian, H. Wu^*, “Monolithic 3D Integration of Logic, Memory and Computing-In-Memory for One-Shot Learning”, IEDM Tech. Dig., 21.5.1-21.5.4 (2021).

[10] H. Zhao, Z. Liu, J. Tang^*, B. Gao, Y. Zhou, P. Yao, Y. Xi, H. Qian, H. Wu^*, “Implementation of Discrete Fourier Transform using RRAM Arrays with Quasi-Analog Mapping for High-Fidelity Medical Image Reconstruction”, IEDM Tech. Dig., 12.4.1-12.4.4 (2021).

[11] X. Li^#, J. Tang^#, Q. Zhang^#, B. Gao, J. Joshua Yang, S. Song, W. Wu, W. Zhang, P. Yao, N. Deng, L. Deng, Y. Xie, H. Qian, H. Wu^*, “Power-Efficient Neural Network with Artificial Dendrites”, Nature Nanotechnology, 15, 776 (2020).

[12] Z. Liu, J. Tang^*, B. Gao, X. Li, P. Yao, Y. Lin, D. Liu, B. Hong, H. Qian, H. Wu^*, “Multi-Channel Parallel Processing of Neural Signals in Memristor Arrays”, Science Advances, 6, eabc4797 (2020).

[13] Z. Liu, J. Tang^*, B. Gao, P. Yao, X. Li, D. Liu, Y. Zhou, H. Qian, B. Hong^*, H. Wu^*, “Neural Signal Analysis with Memristor Arrays Towards High-Efficiency Brain-Machine Interfaces”, Nature Communications, 11, 4234 (2020).

[14] J. Tang^#, F. Yuan^#, X. Shen^#, Z. Wang, M. Rao, Y. He, Y. Sun, X. Li, W. Zhang, Y. Li, B. Gao, H. Qian, G. Bi, S. Song, J. Joshua Yang*, H. Wu*, “Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges”, Advanced Materials, 31, 1902761 (2019).

[15] Y. Lin, Q. Zhang, J. Tang^*, B. Gao, C. Li, P. Yao, Z. Liu, J. Zhu, J. Lu, S. X. Hu, H. Qian, H. Wu^*, “Bayesian Neural Network Realization by Exploiting Inherent Stochastic Behavior of Analog RRAM”, IEDM Tech. Dig., 14.6.1-14.6.4 (2019).

[16] J. Tang^*, Q. Cao, G. Tulevski, K. Jenkins, L. Nela, D. Farmer, S.-J. Han^*, “Flexible CMOS integrated circuits based on carbon nanotubes with sub-10 ns stage delays”, Nature Electronics, 1, 191 (2018).

[17] L. Nela^#, J. Tang^#*, Q. Cao, G. Tulevski, S.-J. Han^*, “Large-Area High-Performance Flexible Pressure Sensor with Carbon Nanotube Active Matrix for Electronic Skin”, Nano Lett., 18, 2054 (2018).

[18] S.-J. Han^#*, J. Tang^#, B. Kumar, A. Falk, D. Farmer, G. Tulevski, K. Jenkins, S. Oida, J. Ott, J. Hannon, W. Haensch, “High-Speed Logic Integrated Circuits with Solution-Processed Self-Assembled Carbon Nanotubes”, Nature Nanotech., 12, 861 (2017).