Jian Shen
shenj5494@fudan.edu.cn
Education
1993-1996 Ph.D. in [Magnetism], [Max Planck Institute of Microstructure Physics (Halle)]
1989-1992 Master's Degree in [Surface Physics], [Laboratory of Nanoscale Physics and Devices, Institute of Physics, Chinese Academy of Sciences]
1985-1989 Bachelor's Degree in [Solid state physics], [Zhejiang University]
Experience
2023-Present Senior Researcher, Hefei National Laboratory
2020-Present Director, National Key Laboratory of Applied Surface Physics, Fudan University
2017-Present Dean, Institute for Nanoelectronic Devices and Quantum Computing, Fudan University
2009-Present Distinguished "Haoqing" Professor, Department of Physics, Fudan University*
2009-2020 Chair, Department of Physics, Fudan University
1998-2009 Research Scientist, Oak Ridge National Laboratory, USA
1996-1998 Postdoctoral/Group Leader, Max Planck Institute of Microstructure Physics (Halle), Germany
Overview of Academic Research
Focusing on quantum materials as the core research target, we integrate thin-film growth and micro/nanofabrication techniques to construct low-dissipation nanoscale devices with tunable fluctuation characteristics, laying the foundation for next-generation physically intelligent computing. Our research primarily explores spin quantum materials and two-dimensional heterostructures to develop various computing units that exhibit intrinsic nonlinearity and high energy efficiency. We were the first to realize a reservoir computing network based on artificial spin ice. Additionally, we developed a prototype multi-node magnonic Hopfield network using spin materials, which successfully demonstrated associative memory for low-orthogonality patterns. Furthermore, we exploited thermal fluctuation properties inherent in quantum materials to construct highly stable probabilistic bits (p-bits), offering a novel approach for physically driven probabilistic computing platforms. Building on this foundation, we further investigate the control of thermally fluctuating nanodevices through electric, magnetic, and optical fields to fabricate integrable p-bit units. By constructing interconnected device networks, we explore their scalability and computational potential as core modules for probabilistic computing. Our goal is to overcome the energy and efficiency limitations of the traditional von Neumann architecture, thereby providing both experimental and theoretical support for building a new class of neuromorphic computing systems based on physical fluctuations.
Representative Publications
1. Yadi Wang, et al. Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire,National Science Review 12, nwae 338 (2024)
2. Chang Niu, et al. A self- learning magnetic Hopffeld neural network with intrinsic gradient descent adaption, PNAS, 121 51 (2024)
3. Wenjie Hu, et al. Distinguishing artificial spin ice states using magnetoresistance effect for neuromorphic computing, Nat. Commun. 14,2562 (2023)
4. Qiang Li, et al. Electronically phase separated nano-network in antiferromagnetic insulating LaMnO3/PrMnO3/CaMnO3 tricolor superlattice, Nat. Commun. 13, 6593 (2022)
5. Mei Fang, et al. Tuning the interfacial spin-orbit coupling with ferroelectricity, Nat. Commun. 11, 2627 (2020)
6. Tian Miao, et al. Direct Experimental Evidence of Physical Origin of Electronic Phase Separation in Manganites, PNAS 117, 7090 (2020)
7. Wenting Yang, et al. Achieving large and nonvolatile tunablemagnetoresistance in organic spin valves usingelectronic phase separated manganites, Nat. Commun. 10, 3877 (2019)