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1.
Although the structure and function of the human brain are still far from being fully understood, brain‐inspired computing architectures mainly consisting of artificial neurons and artificial synapses have been attracting more and more attentions due to their powerful computing capability and energy efficient operation. Synaptic plasticity is believed to be the origin of learning and memory. However, it is still a big challenge to realize artificial synapses with high reliability, good scalability, and low energy consumption, comparable to their biological counterparts. The memristor is a two‐terminal electronic device whose conductance can be reversibly regulated by electric stimuli. Memristive devices are considered ideal synaptic emulators due to their superior performance such as high speed and low power operation. This work reviews the recent advances in the development of memristive synapses based on different types of memristors. First, various working mechanisms of memristive synapses are discussed and compared. Then, different integration approaches of synaptic devices are described and compared. Various cognitive functions implemented with synaptic crossbar circuits are also described. Finally, the approaches for optimizing the performance parameters of memristive synapses and challenges to integrate the synaptic devices with complementary metal oxide semiconductor (CMOS) or memristive neurons are overviewed and discussed briefly. 相似文献
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Jianshi Tang Fang Yuan Xinke Shen Zhongrui Wang Mingyi Rao Yuanyuan He Yuhao Sun Xinyi Li Wenbin Zhang Yijun Li Bin Gao He Qian Guoqiang Bi Sen Song J. Joshua Yang Huaqiang Wu 《Advanced materials (Deerfield Beach, Fla.)》2019,31(49)
As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions. 相似文献
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Navnidhi K. Upadhyay Hao Jiang Zhongrui Wang Shiva Asapu Qiangfei Xia J. Joshua Yang 《Advanced Materials Technologies》2019,4(4)
A neuromorphic computing system may be able to learn and perform a task on its own by interacting with its surroundings. Combining such a chip with complementary metal–oxide–semiconductor (CMOS)‐based processors can potentially solve a variety of problems being faced by today's artificial intelligence (AI) systems. Although various architectures purely based on CMOS are designed to maximize the computing efficiency of AI‐based applications, the most fundamental operations including matrix multiplication and convolution heavily rely on the CMOS‐based multiply–accumulate units which are ultimately limited by the von Neumann bottleneck. Fortunately, many emerging memory devices can naturally perform vector matrix multiplication directly utilizing Ohm's law and Kirchhoff's law when an array of such devices is employed in a cross‐bar architecture. With certain dynamics, these devices can also be used either as synapses or neurons in a neuromorphic computing system. This paper discusses various emerging nanoscale electronic devices that can potentially reshape the computing paradigm in the near future. 相似文献
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Lu'an Liu Jianhui Zhao Gang Cao Shukai Zheng Xiaobing Yan 《Advanced Materials Technologies》2021,6(12):2100373
With the advancement of artificial intelligence technology, more and more biological functions need to be imitated to complete more complex tasks and adapt to a complex external work environment. Memristors, as an excellent candidate for neuromorphic artificial electronic devices with many biological functions, have inspired the interest of researchers because of the advantages of scalability, good retention, and high operating speed. In this work, wide band gap semiconductor materials silicon carbide (SiC) films are prepared as a memristor medium. By adjusting the current compliance, both threshold character and bipolar resistive switching phenomenon are realized in one device with both lower powers for set operation. For the threshold characteristic, this device has mimicked the “threshold,” “inadaptation,” and “relaxation” features of a nociceptor, which will protect the artificial intelligence system to have stronger adaptability to the external environment. For the bipolar resistive switching characteristics, this device demonstrates good stability and retention time, with a switching speed of 18 ns. These bipolar resistance switching characteristics have simulated many synaptic functions. Pulses with hundreds of nanosecond time scale widths are conducive to fast learning and calculation. This device-based third-generation SiC semiconductor material will find a broad application in neuromorphic chip systems. 相似文献
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Minkyung Lee Woobin Lee Seungbeom Choi Jeong‐Wan Jo Jaekyun Kim Sung Kyu Park Yong‐Hoon Kim 《Advanced materials (Deerfield Beach, Fla.)》2017,29(28)
The combination of a neuromorphic architecture and photonic computing may open up a new era for computational systems owing to the possibility of attaining high bandwidths and the low‐computation‐power requirements. Here, the demonstration of photonic neuromorphic devices based on amorphous oxide semiconductors (AOSs) that mimic major synaptic functions, such as short‐term memory/long‐term memory, spike‐timing‐dependent plasticity, and neural facilitation, is reported. The synaptic functions are successfully emulated using the inherent persistent photoconductivity (PPC) characteristic of AOSs. Systematic analysis of the dynamics of photogenerated carriers for various AOSs is carried out to understand the fundamental mechanisms underlying the photoinduced carrier‐generation and relaxation behaviors, and to search for a proper channel material for photonic neuromorphic devices. It is found that the activation energy for the neutralization of ionized oxygen vacancies has a significant influence on the photocarrier‐generation and time‐variant recovery behaviors of AOSs, affecting the PPC behavior. 相似文献
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Xiumei Wang Huihuang Yang Enlong Li Chunbin Cao Wen Zheng Huipeng Chen Wenwu Li 《Small (Weinheim an der Bergstrasse, Germany)》2023,19(18):2205395
Stretchable synaptic transistors, a core technology in neuromorphic electronics, have functions and structures similar to biological synapses and can concurrently transmit signals and learn. Stretchable synaptic transistors are usually soft and stretchy and can accommodate various mechanical deformations, which presents significant prospects in soft machines, electronic skin, human–brain interfaces, and wearable electronics. Considerable efforts have been devoted to developing stretchable synaptic transistors to implement electronic device neuromorphic functions, and remarkable advances have been achieved. Here, this review introduces the basic concept of artificial synaptic transistors and summarizes the recent progress in device structures, functional-layer materials, and fabrication processes. Classical stretchable synaptic transistors, including electric double-layer synaptic transistors, electrochemical synaptic transistors, and optoelectronic synaptic transistors, as well as the applications of stretchable synaptic transistors in light-sensory systems, tactile-sensory systems, and multisensory artificial-nerves systems, are discussed. Finally, the current challenges and potential directions of stretchable synaptic transistors are analyzed. This review presents a detailed introduction to the recent progress in stretchable synaptic transistors from basic concept to applications, providing a reference for the development of stretchable synaptic transistors in the future. 相似文献
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Sang Hyun Sung Do Hyun Kim Tae Jin Kim Il‐Suk Kang Keon Jae Lee 《Advanced Materials Technologies》2019,4(4)
The latest progresses in software engineering such as cloud computing, big data analysis, and machine learning have accelerated the emergence of advanced intelligent systems (AIS). However, the current computing system has significant challenges in dealing with unstructured data (e.g., image, voice, physiological signals) because the von‐Neumann bottleneck induces latency and power consumption issues. Neuromorphic computing, which imitates the behaviors of neuron and synapse within the biological neural network, is considered a promising solution beyond von‐Neumann architecture, since its collocated structure of processor and memory enables parallel processing of unstructured data with remarkable efficiency. Memristors are considered as next‐generation nonvolatile memory devices due to fast speed, low power, and excellent scalability. However, a low reliability and leakage current issues remain as obstacle to the commercialization of memristors. Memristive devices have been widely investigated as a strong candidate for artificial synapses since their resistance modulation characteristics under electrical stimulus are analogous to the plasticity of the brain synapse. Although emulation of synaptic behavior by single memristor cells is demonstrated by many researchers, the development of fully functional memristive neural network requires further investigations. This paper introduces the recent advances and developments in the field of inorganic‐based unconventional memristive devices for future AIS applications. 相似文献
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Juan Carlos Gonzalez-Rosillo Moran Balaish Zachary D. Hood Neel Nadkarni Dimitrios Fraggedakis Kun Joong Kim Kaitlyn M. Mullin Reto Pfenninger Martin Z. Bazant Jennifer L. M. Rupp 《Advanced materials (Deerfield Beach, Fla.)》2020,32(9):1907465
Specialized hardware for neural networks requires materials with tunable symmetry, retention, and speed at low power consumption. The study proposes lithium titanates, originally developed as Li-ion battery anode materials, as promising candidates for memristive-based neuromorphic computing hardware. By using ex- and in operando spectroscopy to monitor the lithium filling and emptying of structural positions during electrochemical measurements, the study also investigates the controlled formation of a metallic phase (Li7Ti5O12) percolating through an insulating medium (Li4Ti5O12) with no volume changes under voltage bias, thereby controlling the spatially averaged conductivity of the film device. A theoretical model to explain the observed hysteretic switching behavior based on electrochemical nonequilibrium thermodynamics is presented, in which the metal-insulator transition results from electrically driven phase separation of Li4Ti5O12 and Li7Ti5O12. Ability of highly lithiated phase of Li7Ti5O12 for Deep Neural Network applications is reported, given the large retentions and symmetry, and opportunity for the low lithiated phase of Li4Ti5O12 toward Spiking Neural Network applications, due to the shorter retention and large resistance changes. The findings pave the way for lithium oxides to enable thin-film memristive devices with adjustable symmetry and retention. 相似文献
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Alan Molinari Ralf Witte Krishna Kanth Neelisetty Saleh Gorji Christian Kübel Ingo Münch Franziska Wöhler Lothar Hahn Stefan Hengsbach Klaus Bade Horst Hahn Robert Kruk 《Advanced materials (Deerfield Beach, Fla.)》2020,32(12):1907541
Ferroelectric oxide memristors are currently in the highlights of a thriving area of research aiming at the development of nonvolatile, adaptive memories for applications in neuromorphic computing. However, to date a precise control of synapse-like functionalities by adjusting the interplay between ferroelectric polarization and resistive switching processes is still an ongoing challenge. Here, it is shown that by means of controlled electron beam radiation, a prototypical ferroelectric film of BaTiO3 can be turned into a memristor with multiple configurable resistance states. Ex situ and in situ analyses of current/voltage characteristics upon electron beam exposure confirm the quasi-continuous variation of BaTiO3 resistance up to two orders of magnitude under the typical experimental conditions employed in electron beam patterning and characterization techniques. These results demonstrate an unprecedented effective route to locally and scalably engineering multilevel ferroelectric memristors via application of moderate electron beam radiation. 相似文献
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Kyunghyun Kim Chia‐Ling Chen Quyen Truong Alex M. Shen Yong Chen 《Advanced materials (Deerfield Beach, Fla.)》2013,25(12):1693-1698
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Kyunghyun Kim Chia‐Ling Chen Quyen Truong Alex M. Shen Yong Chen 《Advanced materials (Deerfield Beach, Fla.)》2013,25(12):1692-1692
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Fuqin Sun Qifeng Lu Lin Liu Lianhui Li Yingyi Wang Mingming Hao Zhiguang Cao Zihao Wang Shuqi Wang Tie Li Ting Zhang 《Advanced Materials Technologies》2020,5(1)
A prominent challenge for artificial synaptic devices toward artificial perception systems is hardware redundancy, which demands neuromorphic devices that integrate both sensing and processing functions. Inspired by the biological visual and nervous systems, a novel flexible, dual‐modulation synaptic field‐effect transistor (SFET) is demonstrated in this work. The flexible SFET is constructed with zinc oxide nanowires and sodium alginate, which acts as the semiconductor layer and the gate dielectric, respectively. An excitatory postsynaptic current in this artificial synapse can be triggered by both electrical and ultraviolet stimuli as presynaptic spikes as a result of the electric double layer effect and the photoelectric effect. More importantly, through the co‐modulation of light and electric stimuli, the memory level of the artificial synapses can be tuned based on the transformation between short‐term plasticity and long‐term plasticity initiated by the gate voltage. Different voltages can modulate the memory retention levels of the optical inputs similar to the function of the optic nerve system. The underlying mechanisms for the SFET are investigated using Fourier transform infrared spectroscopy, photoluminescence, and X‐ray photoelectron spectroscopy. Overall, the devices provide a novel idea to mimic visual memory, showing a promising strategy for future electronic eyes. 相似文献
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Sanghyeon Choi Jehyeon Yang Gunuk Wang 《Advanced materials (Deerfield Beach, Fla.)》2020,32(51):2004659
Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing. 相似文献
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Delu Chen Xinrong Zhi Yifan Xia Shuhan Li Benbo Xi Chun Zhao Xin Wang 《Small (Weinheim an der Bergstrasse, Germany)》2023,19(36):2301196
Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information-processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr3-based memristor demonstrates a high ON/OFF ratio (>103), long retention (>104 s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5 × 5 spinosum-structured piezoresistive sensor array (sensitivity of 22.4 kPa−1, durability of 1.5 × 104 cycles, and fast response time of 2.43 ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr3-based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5 × 5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital−analog bimodal memristor would demonstrate potential application in human–machine interaction, prosthetics, and artificial intelligence. 相似文献
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Zhengjin Weng;Haofei Zheng;Lingqi Li;Wei Lei;Helong Jiang;Kah-Wee Ang;Zhiwei Zhao; 《Small (Weinheim an der Bergstrasse, Germany)》2024,20(5):2304518
Designing reliable and energy-efficient memristors for artificial synaptic arrays in neuromorphic computing beyond von Neumann architecture remains a challenge. Here, memristors based on emerging layered nickel phosphorus trisulfide (NiPS3) are reported that exhibit several favorable characteristics, including uniform bipolar nonvolatile switching with small operating voltage (<1 V), fast switching speed (< 20 ns), high On/Off ratio (>102), and the ability to achieve programmable multilevel resistance states. Through direct experimental evidence using transmission electron microscopy and energy dispersive X-ray spectroscopy, it is revealed that the resistive switching mechanism in the Ti/NiPS3/Au device is related to the formation and dissolution of Ti conductive filaments. Intriguingly, further investigation into the microstructural and chemical properties of NiPS3 suggests that the penetration of Ti ions is accompanied by the drift of phosphorus-sulfur ions, leading to induced P/S vacancies that facilitate the formation of conductive filaments. Furthermore, it is demonstrated that the memristor, when operating in quasi-reset mode, effectively emulates long-term synaptic weight plasticity. By utilizing a crossbar array, multipattern memorization and multiply-and-accumulate (MAC) operations are successfully implemented. Moreover, owing to the highly linear and symmetric multiple conductance states, a high pattern recognition accuracy of ≈96.4% is demonstrated in artificial neural network simulation for neuromorphic systems. 相似文献
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Qingzhou Wan Mohammad T. Sharbati John R. Erickson Yanhao Du Feng Xiong 《Advanced Materials Technologies》2019,4(4)
In today's era of big‐data, a new computing paradigm beyond today's von‐Neumann architecture is needed to process these large‐scale datasets efficiently. Inspired by the brain, which is better at complex tasks than even supercomputers with much better efficiency, the field of neuromorphic computing has recently attracted immense research interest and can have a profound impact in next‐generation computing. Unlike modern computers that use digital “0” and “1” for computation, biological neural networks exhibit analog changes in synaptic connections during the decision‐making and learning processes. Currently, the neuron node is usually implemented by dozens of silicon transistors, an approach that is energy‐intensive and nonscalable. In this paper, recent developments of synaptic electronics for the hardware implementation and acceleration of artificial neural networks will be discussed. Learning mechanisms and synaptic plasticity in the brain and the device level requirements for synaptic electronics will briefly be reviewed, emphasizing the nuance compared to requirements for nonvolatile memories. Several categories of emerging synaptic devices based on phase change memory, resistive memory, electrochemical devices, and 2D devices will be introduced, as well as their associated advantages, disadvantages, and future prospects. 相似文献
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Rohit Abraham John Fucai Liu Nguyen Anh Chien Mohit R. Kulkarni Chao Zhu Qundong Fu Arindam Basu Zheng Liu Nripan Mathews 《Advanced materials (Deerfield Beach, Fla.)》2018,30(25)
Emulation of brain‐like signal processing with thin‐film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di‐chalcogenide (MoS2) neuristors are designed to mimic intracellular ion endocytosis–exocytosis dynamics/neurotransmitter‐release in chemical synapses using three approaches: (i) electronic‐mode: a defect modulation approach where the traps at the semiconductor–dielectric interface are perturbed; (ii) ionotronic‐mode: where electronic responses are modulated via ionic gating; and (iii) photoactive‐mode: harnessing persistent photoconductivity or trap‐assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge‐trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve “plasticity of plasticity–metaplasticity” via dynamic control of Hebbian spike‐time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures. 相似文献