Intelligent Computing Technology Based on New Types of Memristors, Including Materials, Components, Chips, Software, and Applications

[1] Basic research into fuzzy reasoning based on memristor arrays, supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61804171), project leader.

[2] Cutting-edge research of basic components and systems of in-memory computing, supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDPB12), project leader.

[3] Technologies of the architecture, circuit, and component co-design of high-performance deep neural network processing, supported by the Management Program for Emergency of the National Science Foundation of China (Grant No. 61751401), project leader.

[4] Key technologies in brain-inspired computing chips with a million neurons based on asynchronous communication architecture, supported by the National Natural Science Foundation Regional Innovation and Development Joint Fund (Grant No. U20A20220), project leader.

[5] Research and development of materials for high-performance memristor and intelligent infrared sensing devices, supported by the National Basic Research Program of China, project leader.

[6] Research, development, and applications of brain-inspired chips with many-core architecture and based on in-memory computing components, supported by the "Pioneer" R&D project of Zhejiang Provincial Department of Science and Technology, project leader.

[7] Key standards and chip verification of neural network processors, supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2018AAA0103300), leader of a specialized subject.

[1] Shi T, Wang R, Wu Z, Sun Y, An J, Liu Q*, A Review of Resistive Switching Devices: Performance Improvement, Characterization, and Applications [J]. Small Structures 2021, 2 (4), 2000109.

[2] Shi T, Wu J, Liu Y, Yang R*, and Guo X*, Behavioral Plasticity Emulated with Lithium Lanthanum Titanate-Based Memristive Devices: Habituation [J]. Advanced Electronic Materials, 2017, 3(9): 1700046.

[3] Shi T, Yang R*, and Guo X*, Coexistence of analog and digital resistive switching in BiFeO3-based memristive devices [J]. Solid State Ionics, 2016, 296(15): 114~119.

[4] Shi T, Yin X, Yang R*, and Guo X*, Pt/WO3/FTO memristive devices with recoverable pseudo-electroforming for time-delay switches in neuromorphic computing [J]. Physical Chemistry Chemical Physics, 2016, 18(14): 9338~9343.

[5] Shi T, Chen Y and Guo X*, Defect Chemistry of Alkaline Earth Metal（Sr/Ba）Titanates [J], Progress in Materials Science, 2016, 80: 77~132

[8] Shi T, Liu Q. Chapter 2. Characteristics and Mechanisms in Resistive Switching Memories in Photo-electroactive Non-volatile Memories for Data Storage and Neuromorphic Computing [M]. Elsevier, 2020.

[9] Wang R, Shi T*, Zhang X, Wu Z, Liu Q, A Dual-functional Ta/TaOx/Ru Device with Both Nonlinear Selector and Resistive Switching Behaviors [J]. RSC Advances 2021, 11 (30), 18241-18245.

[10] Lu J, Wu Z, Zhang X, Wei J, Fang Y, Shi T*, Liu Q. Solving NP-hard Problem by Using Read Noise in Memristive Hopfield Neural Network [J]. IEEE Electron Device Letters, 2020, 41 (11), 1688-1691.

[11] Wu Z, Lu J, Zhao X, Shi T*, Zhang X, Yang Y, Lu J, Wei J, Wang R, Li Y, Liu Q*, Liu M. A Habituation Sensory Nervous System with Memristors [J]. Advanced Materials, 2020, 32 (46), 2004398.

[12] Fang Y, Shi T*, Zhang X, Wu Z, Wei J, Lu J, Liu Q*, Liu M. Impact of Ta/Ti Electrodes on Linearities of TaOx-based Resistive Random-access Memories for Neuromorphic Computing [J]. Science China Physics, Mechanics, Astronomy and Astrophysics, 2020, 63 (9), 297311.

[13] Wu F, Si S, Cao P, Wei W, Zhao X, Shi T*, Zhang X, Ma J, Cao R, Liao L, Tseng T, Liu Q. Interface Engineering via MoS2 Insertion Layer for Improving Resistive Switching of Conductive-Bridging Random Access Memory [J]. Advanced Electronic Materials, 2019, 5(4): 1800747.

[14] Wang R, Shi T*, Zhang X, Wang W, Wei J, Lu J, Zhao X, Wu Z, Cao R, Long S, Liu Q*, Liu M. Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing [J]. Materials, 2018, 11(11): 2102.

[15] Wang W, Wang R, Shi T, Wei J, Cao R, Zhao X, Wu Z, Zhang X, Lu J, Xu H, Li Q*, Liu Q*, Liu M. A Self-Rectification and Quasi-Linear Analogue Memristor for Artificial Neural Networks [J]. IEEE Electron Device Letters, 2019, 40(9): 1407-10.

[16] Wang W, Wu Z, Shi T, Wang Y, Liu S, Cao R, Xu H, Liu Q*, Li Q*. Voltage-control oscillator based on Pt/C/NbOx/TiN device with highly improved threshold switching performances [J]. Science China Physics, Mechanics, Astronomy and Astrophysics, 2019, 62(12): 127821.

[17] Wang Y, Huang F, Hu Y, Cao R, Shi T, Liu Q*, Bi L*, Liu M, Proton Radiation Effects on Y-Doped HfO2 Based Ferroelectric Memory [J]. IEEE Electron Device Letters, 2018, 39, 823-826.

[18] Wu F, Si S, Shi T, Zhao X, Liu Q*, Liao L*, Lv H, Long S, Liu M, Negative Differential Resistance Effect Induced by Metal Ion Implantation in SiO2 Film for Multilevel RRAM Application [J]. Nanotechnology, 2018, 29, 054001.

[19] Zhang M, He L, Shi T, Zha R*. Nanocasting and Direct Synthesis Strategies for Mesoporous Carbons as Supercapacitor Electrodes [J]. Chemistry of Materials, 2018, 30(21): 7391-412.

[20] Zha R, Chen M, Shi T, Nadimicherla R, Jian T, Zhang Z, Zhang M*. Double Dimensionally Ordered Nanostructures: toward a Multifunctional Reinforcing Nanohybrid for Epoxy Resin [J]. RSC Advances, 2017, 7(2): 1177-90.

[21] Zha R, Shi T, Zhang Z, Xu D, Jiang T, Zhang M*. Quasi-reverse-emulsion-templated Approach for a Facile and Sustainable Environmental Remediation for Cadmium [J]. RSC Advances, 2017, 7(11): 6345-57.

[22] Yang R*, Huang H, Hong Q, Yin X, Tan Z, Shi T, Zhou Y, Miao X, Wang X*, Mi S, Jia C, Guo X*. Synaptic Suppression Triplet‐STDP Learning Rule Realized in Second‐Order Memristors [J]. Advanced Functional Materials, 2018, 28 (5).

Dr. SHI’s team focuses on in-memory computing, brain-inspired computing and related fields. The team conducts cutting-edge research on materials, devices, circuits, chips, algorithms, and software related to advanced intelligent devices. We are recruiting postdoctoral fellows, research specialists, and engineers who are interested in:

1.    Advanced sensing, storage, and computing materials and devices

2.    Analog and digital integrated circuit design

3.    Hardware testing system design and development

4.    Applications of machine learning algorithms

5.    Compiling and simulation software design and development