A5057584787- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Photoreceptor and optogenetics research
- Neural Networks and Reservoir Computing
- Transition Metal Oxide Nanomaterials
- Semiconductor materials and devices
- CCD and CMOS Imaging Sensors
- ZnO doping and properties
- Electronic and Structural Properties of Oxides
- Advanced Neural Network Applications
- Advanced Chemical Sensor Technologies
- Neural Networks and Applications
- EEG and Brain-Computer Interfaces
- Video Surveillance and Tracking Methods
- Advanced Sensor and Energy Harvesting Materials
- Phase-change materials and chalcogenides
- Quantum Computing Algorithms and Architecture
- Conducting polymers and applications
- Olfactory and Sensory Function Studies
- Advancements in Semiconductor Devices and Circuit Design
- Physical Unclonable Functions (PUFs) and Hardware Security
- Advanced Image and Video Retrieval Techniques
- Speech and Audio Processing
Peking University
2016-2025
Chinese Institute for Brain Research
2021-2025
Beijing Academy of Artificial Intelligence
2020-2025
Peking University Shenzhen Hospital
2025
Sichuan University
2020-2024
East China University of Technology
2023-2024
Southwest University
2024
China University of Mining and Technology
2024
Dalian Maritime University
2023-2024
Northeast Electric Power University
2024
Through a simple industrialized technique which was completely fulfilled at room temperature, we have developed kind of promising nonvolatile resistive switching memory consisting Ag/ZnO:Mn/Pt with ultrafast programming speed 5 ns, an ultrahigh ROFF/RON ratio 107, long retention time more than 107 s, good endurance, and high reliability elevated temperatures. Furthermore, successfully captured clear visualization nanoscale Ag bridges penetrating through the storage medium, could account for...
Nanoscale metal inclusions in or on solid-state dielectrics are an integral part of modern electrocatalysis, optoelectronics, capacitors, metamaterials and memory devices. The properties these composite systems strongly depend the size, dispersion their chemical stability, usually considered constant. Here we demonstrate that nanoscale (for example, clusters) dynamically change shape, size position upon applied electric field. Through systematic situ transmission electron microscopy studies,...
Abstract Resistive switching (RS) is an interesting property shown by some materials systems that, especially during the last decade, has gained a lot of interest for fabrication electronic devices, with nonvolatile memories being those that have received most attention. The presence and quality RS phenomenon in system can be studied using different prototype cells, performing experiments, displaying figures merit, developing computational analyses. Therefore, real usefulness impact findings...
Abstract Neuromorphic computing represents an innovative technology that can perform intelligent and energy‐efficient computation, whereas construction of neuromorphic systems requires biorealistic synaptic elements with rich dynamics be tuned based on a robust mechanism. Here, ionic‐gating‐modulated transistor layered crystals transitional metal dichalcogenides phosphorus trichalcogenides is demonstrated, which produce diversity short‐term long‐term plasticity including excitatory...
Brain-inspired neuromorphic computing is expected to revolutionize the architecture of conventional digital computers and lead a new generation powerful paradigms, where memristors with analog resistive switching are considered be potential solutions for synapses. Here we propose demonstrate novel approach engineering linearity in TaOx based memristors, that is, by homogenizing filament growth/dissolution rate via introduction an ion diffusion limiting layer (DLL) at TiN/TaOx interface. This...
This article provides a review of current development and challenges in brain-inspired computing with memristors. We the mechanisms various memristive devices that can mimic synaptic neuronal functionalities survey progress spiking artificial neural networks. Different architectures are compared, including networks, fully connected convolutional Hopfield recurrent Challenges strategies for nanoelectronic systems, device variations, training, testing algorithms, also discussed.
Resistive switching devices (also termed memristive or memristors) are two-terminal nonlinear dynamic electronic that can have broad applications in the fields of nonvolatile memory, reconfigurable logic, analog circuits, and neuromorphic computing. Current rapid advances turn demand better understanding mechanism development physics-based as well simplified device models to guide future designs circuit-level applications. In this article, we review physical processes behind resistive...
Abstract As a key building block of biological cortex, neurons are powerful information processing units and can achieve highly complex nonlinear computations even in individual cells. Hardware implementation artificial with similar capability is great significance for the construction intelligent, neuromorphic systems. Here, we demonstrate an neuron based on NbO x volatile memristor that not only realizes traditional all-or-nothing, threshold-driven spiking spatiotemporal integration, but...
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially algorithm and neural network models. However, it performance hardware, particular energy efficiency a computing system that sets fundamental limit capability learning. Data-centric requires revolution hardware systems, since traditional digital computers based on transistors von Neumann architecture were not purposely designed for neuromorphic computing. A platform...
Abstract Neuromorphic perception systems inspired by biology have tremendous potential in efficiently processing multi-sensory signals from the physical world, but a highly efficient hardware element capable of sensing and encoding multiple is still lacking. Here, we report spike-based neuromorphic system consisting calibratable artificial sensory neurons based on epitaxial VO 2 , where high crystalline quality leads to significantly improved cycle-to-cycle uniformity. A calibration resistor...
Abstract Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing conventional von Neumann machines does not support the requirements emerging applications that extensively large sets data. More recent paradigms, such as high parallelization near-memory computing, help alleviate data bottleneck to some...
Dynamic physical systems such as reservoir computing (RC) architectures show a great prospect in temporal information processing, whereas hierarchical processing capability is still lacking due to the absence of advanced multilayer elements. Here, stackable system constructed based on ferroelectric α-In2 Se3 devices with voltage input and output, which realized by dynamic division between field-effect transistor planar device therefore allows reservoirs cascade, enabling RC. Fast Fourier...
Abstract Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of data presents challenges for traditional systems. Here, we propose highly efficient neuromorphic system based on VO 2 memristors. The volatile positive/negative symmetric threshold switching characteristics memristors are leveraged to construct sparse-spiking yet high-fidelity...
A giant electromechanical d33 coefficient 110pC∕N is obtained in ferroelectric V-doped ZnO films, which nearly one order of magnitude higher than that undoped samples. It considered the switchable spontaneous polarization induced by V dopants and accompanying relatively high permittivity should be responsible for enhancement piezoelectric response. Moreover, from another point view, an easier rotation V–O bonds are noncollinear with c axis under electric field might microscopic origin this...
Resistive switching devices are widely believed as a promising candidate for future memory and logic applications. Here we show that by using multilayer oxide heterostructures the characteristics can be systematically controlled, ranging from unipolar to complementary bipolar with linear nonlinear on-states high endurance. Each layer tailed specific function during resistance switching, thus greatly improving degree of control flexibility optimized device performance.
Complementary resistive switches (CRS) are considered as a potential solution for the sneak path problem in large-scale integration of passive crossbar memory arrays. A typical CRS is composed two bipolar cells that connected anti-serially. Here, we report tantalum-oxide based achieves complementary switching functionality within single cell. The effect accompanied by polarity reversal different voltage bias regimes. These effects were explained redistribution oxygen vacancies inside layers....