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Articles de revues sur le sujet « Neuromorphic devices »

Auteur : Grafiati

Publié le 14 décembre 2024

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1

Ielmini, Daniele, and Stefano Ambrogio. "Emerging neuromorphic devices." Nanotechnology 31, no. 9 (2019): 092001. http://dx.doi.org/10.1088/1361-6528/ab554b.

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Guo, Zhonghao. "Synaptic device-based neuromorphic computing in artificial intelligence." Applied and Computational Engineering 65, no. 1 (2024): 253–59. http://dx.doi.org/10.54254/2755-2721/65/20240511.

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The application of synaptic device-based neuromorphic computing in artificial intelligence is an emerging research field aimed at simulating the structure and function of the human brain and realizing high-efficiency, low-power, and adaptive intelligent computing. This paper reviews the principles, growth and challenges of neuromorphic devices based on synapses computing and its applications and perspectives in artificial intelligence fields like an image processing as well as natural language processing. The paper first introduces the basic concepts, properties and classification of synaptic

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Park, Jisoo, Jihyun Shin, and Hocheon Yoo. "Heterostructure-Based Optoelectronic Neuromorphic Devices." Electronics 13, no. 6 (2024): 1076. http://dx.doi.org/10.3390/electronics13061076.

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The concept of neuromorphic devices, aiming to process large amounts of information in parallel, at low power, high speed, and high efficiency, is to mimic the functions of human brain by emulating biological neural behavior. Optoelectronic neuromorphic devices are particularly suitable for neuromorphic applications with their ability to generate various pulses based on wavelength and to control synaptic stimulation. Each wavelength (ultraviolet, visible, and infrared) has specific advantages and optimal applications. Here, the heterostructure-based optoelectronic neuromorphic devices are expl

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Huang, Wen, Huixing Zhang, Zhengjian Lin, Pengjie Hang, and Xing’ao Li. "Transistor-Based Synaptic Devices for Neuromorphic Computing." Crystals 14, no. 1 (2024): 69. http://dx.doi.org/10.3390/cryst14010069.

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Currently, neuromorphic computing is regarded as the most efficient way to solve the von Neumann bottleneck. Transistor-based devices have been considered suitable for emulating synaptic functions in neuromorphic computing due to their synergistic control capabilities on synaptic weight changes. Various low-dimensional inorganic materials such as silicon nanomembranes, carbon nanotubes, nanoscale metal oxides, and two-dimensional materials are employed to fabricate transistor-based synaptic devices. Although these transistor-based synaptic devices have progressed in terms of mimicking synaptic

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Lim, Jung Wook, Su Jae Heo, Min A. Park, and Jieun Kim. "Synaptic Transistors Exhibiting Gate-Pulse-Driven, Metal-Semiconductor Transition of Conduction." Materials 14, no. 24 (2021): 7508. http://dx.doi.org/10.3390/ma14247508.

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Neuromorphic devices have been investigated extensively for technological breakthroughs that could eventually replace conventional semiconductor devices. In contrast to other neuromorphic devices, the device proposed in this paper utilizes deep trap interfaces between the channel layer and the charge-inducing dielectrics (CID). The device was fabricated using in-situ atomic layer deposition (ALD) for the sequential deposition of the CID and oxide semiconductors. Upon the application of a gate bias pulse, an abrupt change in conducting states was observed in the device from the semiconductor to

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Diao, Yu, Yaoxuan Zhang, Yanran Li, and Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications." Sensors 23, no. 24 (2023): 9779. http://dx.doi.org/10.3390/s23249779.

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As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructure

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Feng, Chenyin, Wenwei Wu, Huidi Liu, et al. "Emerging Opportunities for 2D Materials in Neuromorphic Computing." Nanomaterials 13, no. 19 (2023): 2720. http://dx.doi.org/10.3390/nano13192720.

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Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials,

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Kim, Dongshin, Ik-Jyae Kim, and Jang-Sik Lee. "Memory Devices for Flexible and Neuromorphic Device Applications." Advanced Intelligent Systems 3, no. 5 (2021): 2000206. http://dx.doi.org/10.1002/aisy.202000206.

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Huang, Yi, Fatemeh Kiani, Fan Ye, and Qiangfei Xia. "From memristive devices to neuromorphic systems." Applied Physics Letters 122, no. 11 (2023): 110501. http://dx.doi.org/10.1063/5.0133044.

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Progress in hardware and algorithms for artificial intelligence (AI) has ushered in large machine learning models and various applications impacting our everyday lives. However, today's AI, mainly artificial neural networks, still cannot compete with human brains because of two major issues: the high energy consumption of the hardware running AI models and the lack of ability to generalize knowledge and self-adapt to changes. Neuromorphic systems built upon emerging devices, for instance, memristors, provide a promising path to address these issues. Although innovative memristor devices and ci

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Machado, Pau, Salvador Manich, Álvaro Gómez-Pau, et al. "Programming Techniques of Resistive Random-Access Memory Devices for Neuromorphic Computing." Electronics 12, no. 23 (2023): 4803. http://dx.doi.org/10.3390/electronics12234803.

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Neuromorphic computing offers a promising solution to overcome the von Neumann bottleneck, where the separation between the memory and the processor poses increasing limitations of latency and power consumption. For this purpose, a device with analog switching for weight update is necessary to implement neuromorphic applications. In the diversity of emerging devices postulated as synaptic elements in neural networks, RRAM emerges as a standout candidate for its ability to tune its resistance. The learning accuracy of a neural network is directly related to the linearity and symmetry of the wei

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Gumyusenge, Aristide, Armantas Melianas, Scott T. Keene, and Alberto Salleo. "Materials Strategies for Organic Neuromorphic Devices." Annual Review of Materials Research 51, no. 1 (2021): 47–71. http://dx.doi.org/10.1146/annurev-matsci-080619-111402.

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Neuromorphic computing is becoming increasingly prominent as artificial intelligence (AI) facilitates progressively seamless interaction between humans and machines. The conventional von Neumann architecture and complementary metal-oxide-semiconductor transistor scaling are unable to meet the highly demanding computational density and energy efficiency requirements of AI. Neuromorphic computing aims to address these challenges by using brain-like computing architectures and novel synaptic memories that coallocate information storage and computation, thereby enabling low latency at high energy

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Milo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive and CMOS Devices for Neuromorphic Computing." Materials 13, no. 1 (2020): 166. http://dx.doi.org/10.3390/ma13010166.

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Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced comput

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Wu, Yuting, Xinxin Wang, and Wei D. Lu. "Dynamic resistive switching devices for neuromorphic computing." Semiconductor Science and Technology 37, no. 2 (2021): 024003. http://dx.doi.org/10.1088/1361-6641/ac41e4.

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Abstract Neuromorphic systems that can emulate the structure and the operations of biological neural circuits have long been viewed as a promising hardware solution to meet the ever-growing demands of big-data analysis and AI tasks. Recent studies on resistive switching or memristive devices have suggested such devices may form the building blocks of biorealistic neuromorphic systems. In a memristive device, the conductance is determined by a set of internal state variables, allowing the device to exhibit rich dynamics arising from the interplay between different physical processes. Not only c

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You Zhou and Shriram Ramanathan. "Mott Memory and Neuromorphic Devices." Proceedings of the IEEE 103, no. 8 (2015): 1289–310. http://dx.doi.org/10.1109/jproc.2015.2431914.

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Zhao, Qing-Tai, Fengben Xi, Yi Han, Andreas Grenmyr, Jin Hee Bae, and Detlev Gruetzmacher. "Ferroelectric Devices for Neuromorphic Computing." ECS Meeting Abstracts MA2022-02, no. 32 (2022): 1183. http://dx.doi.org/10.1149/ma2022-02321183mtgabs.

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Neuromorphic computing inspired by the neural network systems of the human brain enables energy efficient computing for big-data processing. A neural network is formed by thousands or even millions of neurons which are connected by even a higher number of synapses. Neurons communicate with each other through the connected synapses. The main responsibility of synapses is to transfer information from the pre-synaptic to the postsynaptic neurons. Synapses can memorize and process the information simultaneously. The plasticity of a synapse to strengthen or weaken their activity over time make it c

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Yan, Yujie, Xiaomin Wu, Qizhen Chen, et al. "An intrinsically healing artificial neuromorphic device." Journal of Materials Chemistry C 8, no. 20 (2020): 6869–76. http://dx.doi.org/10.1039/d0tc00726a.

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Jué, Emilie, Matthew R. Pufall, Ian W. Haygood, William H. Rippard, and Michael L. Schneider. "Perspectives on nanoclustered magnetic Josephson junctions as artificial synapses." Applied Physics Letters 121, no. 24 (2022): 240501. http://dx.doi.org/10.1063/5.0118287.

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A nanoclustered magnetic Josephson junction (nMJJ) is a hybrid magnetic-superconducting device that can be used as an artificial synapse in neuromorphic applications. In this paper, we review the nMJJ from the device level to the circuit level. We describe the properties of individual devices and show how they can be integrated into a neuromorphic circuit. We discuss the current limitations related to the study of the nMJJ, what can be done to improve the device and better understand the underlying physics, and where the community can focus its efforts to develop magnetic Josephson junctions f

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Lin, Xinhuang, Haotian Long, Shuo Ke, et al. "Indium-Gallium-Zinc-Oxide-Based Photoelectric Neuromorphic Transistors for Spiking Morse Coding." Chinese Physics Letters 39, no. 6 (2022): 068501. http://dx.doi.org/10.1088/0256-307x/39/6/068501.

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The human brain that relies on neural networks communicated by spikes is featured with ultralow energy consumption, which is more robust and adaptive than any digital system. Inspired by the spiking framework of the brain, spike-based neuromorphic systems have recently inspired intensive attention. Therefore, neuromorphic devices with spike-based synaptic functions are considered as the first step toward this aim. Photoelectric neuromorphic devices are promising candidates for spike-based synaptic devices with low latency, broad bandwidth, and superior parallelism. Here, the indium-gallium-zin

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Lee, Jae-Eun, Chuljun Lee, Dong-Wook Kim, Daeseok Lee, and Young-Ho Seo. "An On-Chip Learning Method for Neuromorphic Systems Based on Non-Ideal Synapse Devices." Electronics 9, no. 11 (2020): 1946. http://dx.doi.org/10.3390/electronics9111946.

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In this paper, we propose an on-chip learning method that can overcome the poor characteristics of pre-developed practical synaptic devices, thereby increasing the accuracy of the neural network based on the neuromorphic system. The fabricated synaptic devices, based on Pr1−xCaxMnO3, LiCoO2, and TiOx, inherently suffer from undesirable characteristics, such as nonlinearity, discontinuities, and asymmetric conductance responses, which degrade the neuromorphic system performance. To address these limitations, we have proposed a conductance-based linear weighted quantization method, which control

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Chen, Chao, Tao Lin, Jianteng Niu, et al. "Surface acoustic wave controlled skyrmion-based synapse devices." Nanotechnology 33, no. 11 (2021): 115205. http://dx.doi.org/10.1088/1361-6528/ac3f14.

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Abstract Magnetic skyrmions, which are particle-like spin structures, are promising information carriers for neuromorphic computing devices due to their topological stability and nanoscale size. In this work, we propose controlling magnetic skyrmions by electric-field-excited surface acoustic waves in neuromorphic computing device structures. Our micromagnetic simulations show that the number of created skyrmions, which emulates the synaptic weight parameter, increases monotonically with increases in the amplitude of the surface acoustic waves. Additionally, the efficiency of skyrmion creation

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González Sopeña, Juan Manuel, Vikram Pakrashi, and Bidisha Ghosh. "A Spiking Neural Network Based Wind Power Forecasting Model for Neuromorphic Devices." Energies 15, no. 19 (2022): 7256. http://dx.doi.org/10.3390/en15197256.

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Many authors have reported the use of deep learning techniques to model wind power forecasts. For shorter-term prediction horizons, the training and deployment of such models is hindered by their computational cost. Neuromorphic computing provides a new paradigm to overcome this barrier through the development of devices suited for applications where latency and low-energy consumption play a key role, as is the case in real-time short-term wind power forecasting. The use of biologically inspired algorithms adapted to the architecture of neuromorphic devices, such as spiking neural networks, is

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Park, Jaeyoung. "Neuromorphic Computing Using Emerging Synaptic Devices: A Retrospective Summary and an Outlook." Electronics 9, no. 9 (2020): 1414. http://dx.doi.org/10.3390/electronics9091414.

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In this paper, emerging memory devices are investigated for a promising synaptic device of neuromorphic computing. Because the neuromorphic computing hardware requires high memory density, fast speed, and low power as well as a unique characteristic that simulates the function of learning by imitating the process of the human brain, memristor devices are considered as a promising candidate because of their desirable characteristic. Among them, Phase-change RAM (PRAM) Resistive RAM (ReRAM), Magnetic RAM (MRAM), and Atomic Switch Network (ASN) are selected to review. Even if the memristor device

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Chen, An, Stefano Ambrogio, Pritish Narayanan, et al. "(Invited) Emerging Nonvolatile Memories for Analog Neuromorphic Computing." ECS Meeting Abstracts MA2024-01, no. 21 (2024): 1293. http://dx.doi.org/10.1149/ma2024-01211293mtgabs.

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Emerging non-volatile memory (NVM) devices, such as STT-MRAM, PCM, RRAM, have been explored for embedded memory and storage applications to replace CMOS-based SRAM/DRAM and Flash devices. Recently, many of these memory devices have been utilized for new computing paradigms beyond Boolean logic and von Neumann architectures. For example, in-memory analog computing reduces data movement between computing and memory units and exploits the intrinsic parallelism in memory arrays. It finds a natural application in deep neural network (DNN) accelerators by implementing high-throughput high-efficiency

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Alialy, Sahar, Koorosh Esteki, Mauro S. Ferreira, John J. Boland, and Claudia Gomes da Rocha. "Nonlinear ion drift-diffusion memristance description of TiO2 RRAM devices." Nanoscale Advances 2, no. 6 (2020): 2514–24. http://dx.doi.org/10.1039/d0na00195c.

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Li, Bo, and Guoyong Shi. "A Native SPICE Implementation of Memristor Models for Simulation of Neuromorphic Analog Signal Processing Circuits." ACM Transactions on Design Automation of Electronic Systems 27, no. 1 (2022): 1–24. http://dx.doi.org/10.1145/3474364.

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Since the memristor emerged as a programmable analog storage device, it has stimulated research on the design of analog/mixed-signal circuits with the memristor as the enabler of in-memory computation. Due to the difficulty in evaluating the circuit-level nonidealities of both memristors and CMOS devices, SPICE-accuracy simulation tools are necessary for perfecting the art of neuromorphic analog/mixed-signal circuit design. This article is dedicated to a native SPICE implementation of the memristor device models published in the open literature and develops case studies of applying such a circ

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Li, Tongxuan. "Neuromorphic Devices Based on Two-Dimensional Materials and Their Applications." Highlights in Science, Engineering and Technology 87 (March 26, 2024): 186–91. http://dx.doi.org/10.54097/kxsmsn90.

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Neuromorphic computing, inspired by the human brain, utilizes thin 2D materials like graphene for their unique electronic properties. These materials are crucial in creating efficient, high-performance computing devices. This paper discusses the synthesis methods for 2D materials, including chemical vapor deposition and mechanical exfoliation, and their integration into neuromorphic device architectures such as transistors and memristors. The paper explores how these devices emulate synaptic behaviors and neuronal activities through charge transport mechanisms, ion migration, and the exploitat

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Ho, Tsz-Lung, Keda Ding, Nikolay Lyapunov, et al. "Multi-Level Resistive Switching in SnSe/SrTiO3 Heterostructure Based Memristor Device." Nanomaterials 12, no. 13 (2022): 2128. http://dx.doi.org/10.3390/nano12132128.

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Multilevel resistive switching in memristive devices is vital for applications in non-volatile memory and neuromorphic computing. In this study, we report on the multilevel resistive switching characteristics in SnSe/SrTiO3(STO) heterojunction-based memory devices with silver (Ag) and copper (Cu) top electrodes. The SnSe/STO-based memory devices present bipolar resistive switching (RS) with two orders of magnitude on/off ratio, which is reliable and stable. Moreover, multilevel state switching is achieved in the devices by sweeping voltage with current compliance to SET the device from high re

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YOON, Tae-Sik. "Artificial Synaptic Devices for Neuromorphic Systems." Physics and High Technology 28, no. 4 (2019): 3–8. http://dx.doi.org/10.3938/phit.28.011.

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Liu, Yi-Chun, Ya Lin, Zhong-Qiang Wang, and Hai-Yang Xu. "Oxide-based memristive neuromorphic synaptic devices." Acta Physica Sinica 68, no. 16 (2019): 168504. http://dx.doi.org/10.7498/aps.68.20191262.

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Guo, Yan-Bo, and Li-Qiang Zhu. "Recent progress in optoelectronic neuromorphic devices." Chinese Physics B 29, no. 7 (2020): 078502. http://dx.doi.org/10.1088/1674-1056/ab99b6.

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Chang, Ting, Yuchao Yang, and Wei Lu. "Building Neuromorphic Circuits with Memristive Devices." IEEE Circuits and Systems Magazine 13, no. 2 (2013): 56–73. http://dx.doi.org/10.1109/mcas.2013.2256260.

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Liu, Chang, Ru Huang, Yanghao Wang, and Yuchao Yang. "Progresses and outlook in neuromorphic devices." Chinese Science Bulletin 65, no. 10 (2019): 904–15. http://dx.doi.org/10.1360/tb-2019-0739.

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Sun, Jia, Ying Fu, and Qing Wan. "Organic synaptic devices for neuromorphic systems." Journal of Physics D: Applied Physics 51, no. 31 (2018): 314004. http://dx.doi.org/10.1088/1361-6463/aacd99.

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Zhu, Yixin, Huiwu Mao, Ying Zhu, et al. "CMOS-Compatible Neuromorphic Devices for Neuromorphic Perception and Computing: A Review." International Journal of Extreme Manufacturing, August 11, 2023. http://dx.doi.org/10.1088/2631-7990/acef79.

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Abstract Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient, low-power, and adaptive computing systems by emulating the information processing mechanisms of biological neural systems. At the core of neuromorphic computing are neuromorphic devices that mimic the functions and dynamics of neurons and synapses, enabling the hardware implementation of artificial neural networks. Various types of neuromorphic devices have been proposed based on different physical mechanisms such as resistive switching device and electric-double-layer transistors. These de

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Huang, Zhuohui, Yanran Li, Yi Zhang, Jiewei Chen, Jun He, and Jie Jiang. "2D Multifunctional Devices: from Material Preparation to Device Fabrication and Neuromorphic Applications." International Journal of Extreme Manufacturing, February 28, 2024. http://dx.doi.org/10.1088/2631-7990/ad2e13.

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Abstract Neuromorphic computing systems, which mimic the operation of neurons and synapses in the human brain, are seen as an appealing next-generation computing method due to their strong and efficient computing abilities. Two-dimensional (2D) materials with dangling bond-free surfaces and atomic-level thicknesses have emerged as promising candidates for neuromorphic computing hardware. As a result, 2D neuromorphic devices may provide an ideal platform for developing multifunctional neuromorphic applications. Here, we review the recent neuromorphic devices based on 2D material and their multi

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Shen Liu-feng, Hu Ling-xiang, Kang Feng-wen, Ye Yu-min, and Zhuge Fei. "Optoelectronic neuromorphic devices and their applications." Acta Physica Sinica, 2022, 0. http://dx.doi.org/10.7498/aps.71.20220111.

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Conventional computers based on the von Neumann architecture are inefficient in parallel computing and self-adaptive learning, and therefore cannot meet the rapid development of information technology that needs efficient and high-speed computing. Due to the unique advantages such as high parallelism and ultralow power consumption, bioinspired neuromorphic computing can have the capability to break through the bottlenecks of conventional computers and is now considered as an ideal choice to realize the next-generation artificial intelligence. As the hardware carriers that allow implementing ne

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Long, Yan, Xiang Chen, Xiaoxin Pan, et al. "Memristor Constructed by CsPbIBr₂ inorganic halide perovskite for Artificial Synapse and Logic Operation." physica status solidi (RRL) – Rapid Research Letters, October 31, 2023. http://dx.doi.org/10.1002/pssr.202300342.

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Neuromorphic devices are one of the promising electronic devices that implementing artificial neural networks and substituting for traditional semiconductor devices in recent years. Inorganic halide perovskite (IMHP) is considered as an advantageous material to constitute neuromorphic components. Herein, the CsPbIBr2 memristor displays superior resistive‐switching properties (RS) under various temperature and storage period. In addition, synaptic plasticity, including paired pulse facilitation (PPF) and spiking timing‐dependent plasticity (STDP) is observed for CsPbIBr2 device, whose resistanc

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Zhong, Hai, Kuijuan Jin, and Chen Ge. "Hafnia-based neuromorphic devices." Applied Physics Letters 125, no. 15 (2024). http://dx.doi.org/10.1063/5.0226206.

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The excellent complementary metal-oxide-semiconductor compatibility and rich physicochemical properties of hafnia-based materials, in particular the unique ferroelectricity that surpasses of conventional ferroelectrics, make hafnia-based devices promising candidates for industrial applications. This Perspective examines the fundamental properties of hafnia-based materials relevant to neuromorphic devices, including their dielectric, ferroelectric, antiferroelectric properties, and the associated ultra-high oxygen-ion conductivity. It also reviews neuromorphic devices developed leveraging these

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Shim, Hyunseok, Seonmin Jang, Anish Thukral, et al. "Artificial neuromorphic cognitive skins based on distributed biaxially stretchable elastomeric synaptic transistors." Proceedings of the National Academy of Sciences 119, no. 23 (2022). http://dx.doi.org/10.1073/pnas.2204852119.

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Significance Enabling distributed neurologic and cognitive functions in soft deformable devices, such as robotics, wearables, skin prosthetics, bioelectronics, etc., represents a massive leap in their development. The results presented here reveal the device characteristics of the building block, i.e., a stretchable elastomeric synaptic transistor, its characteristics under various levels of biaxial strain, and performances of various stretchy distributed neuromorphic devices. The stretchable neuromorphic array of synaptic transistors and the neuromorphic imaging sensory skin enable platforms

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Zhang, Zirui, Dongliang Yang, Huihan Li, et al. "2D materials and van der Waals heterojunctions for neuromorphic computing." Neuromorphic Computing and Engineering, August 17, 2022. http://dx.doi.org/10.1088/2634-4386/ac8a6a.

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Abstract Neuromorphic computing systems employing artificial synapses and neurons are expected to overcome the limitations of the present von Neumann computing architecture in terms of efficiency and bandwidth limits. Traditional neuromorphic devices have used 3D bulk materials, and thus, the resulting device size is difficult to be further scaled down for high density integration, which is required for highly integrated parallel computing. The emergence of two-dimensional (2D) materials offers a promising solution, as evidenced by the surge of reported 2D materials functioning as neuromorphic

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Hu, Lingxiang, Xia Zhuge, Jingrui Wang, et al. "Emerging Optoelectronic Devices for Brain‐Inspired Computing." Advanced Electronic Materials, September 9, 2024. http://dx.doi.org/10.1002/aelm.202400482.

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AbstractBrain‐inspired neuromorphic computing is recognized as a promising technology for implementing human intelligence in hardware. Neuromorphic devices, including artificial synapses and neurons, are regarded as essential components for the construction of neuromorphic hardware systems. Recently, optoelectronic neuromorphic devices are increasingly highlighted due to their potential applications in next‐generation artificial visual systems, attributed to their integrated sensing, computing, and memory capabilities. In this review, recent advancements in optoelectronic synapses and neurons

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Chen, H. J., C. C. Chiang, C. Y. Cheng, D. Qu, and S. Y. Huang. "Neuromorphic computing devices based on the asymmetric temperature gradient." Applied Physics Letters 122, no. 26 (2023). http://dx.doi.org/10.1063/5.0155229.

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Neuromorphic computing devices, which emulate biological neural networks, are crucial in realizing artificial intelligence for information processing and decision-making. Different types of neuromorphic computing devices with varying resistance levels have been developed, such as oxide-based memristors caused by ion diffusion, phase transition-based devices caused by threshold switching, progressive crystallization/amorphization, and spintronics-based devices caused by magnetic domain switching. However, these devices face significant challenges, including disruptions in the reading process, l

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Sun, Yilin, Huaipeng Wang, and Dan Xie. "Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications." Nano-Micro Letters 16, no. 1 (2024). http://dx.doi.org/10.1007/s40820-024-01445-x.

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AbstractManipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artificial intelligence. However, great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years. Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation, improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions.

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Gao, Changsong, Di Liu, Chenhui Xu, et al. "Feedforward Photoadaptive Organic Neuromorphic Transistor with Mixed‐Weight Plasticity for Augmenting Perception." Advanced Functional Materials, January 23, 2024. http://dx.doi.org/10.1002/adfm.202313217.

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AbstractOrganic photoelectric neuromorphic devices that mimic the brain are widely explored for advanced perceptual computing. However, current individual neuromorphic synaptic devices mainly focus on utilizing linear models to process optoelectronic signals, which means that there is a lack of effective response to nonlinear structural information from the real world, severely limiting the computational efficiency and adaptability of networks to static and dynamic information. Here, a feedforward photoadaptive organic neuromorphic transistor with mixed‐weight plasticity is reported. By introd

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Gärisch, Fabian, Vincent Schröder, Emil J. W. List‐Kratochvil, and Giovanni Ligorio. "Scalable Fabrication of Neuromorphic Devices Using Inkjet Printing for the Deposition of Organic Mixed Ionic‐Electronic Conductor." Advanced Electronic Materials, November 3, 2024. http://dx.doi.org/10.1002/aelm.202400479.

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AbstractRecent advancements in artificial intelligence (AI) have highlighted the critical need for energy‐efficient hardware solutions, especially in edge‐computing applications. However, traditional AI approaches are plagued by significant power consumption. In response, researchers have turned to biomimetic strategies, drawing inspiration from the ion‐mediated operating principle of biological synapses, to develop organic neuromorphic devices as promising alternatives. Organic mixed ionic‐electronic conductor (OMIEC) materials have emerged as particularly noteworthy in this field, due to the

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Jiang Zi-Han, Ke Shuo, Zhu Ying, et al. "Flexible neuromorphic transistors for bio-inspired perception application." Acta Physica Sinica, 2022, 0. http://dx.doi.org/10.7498/aps.71.20220308.

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Biological perception system has the unique advantages of high parallelism, high error tolerance, self-adaptation and low power consumption. Using neuromorphic devices to emulate biological perceptual system can effectively promote the development of brain-computer interfaces, intelligent perception, biological prosthesis and so on. Compared with other neuromorphic devices, multi-terminal neuromorphic transistors can not only realize signal transmission and training learning at the same time, but also can carry out nonlinear spatio-temporal integration and collaborative regulation of multi-cha

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Lu, Guangming, and Ekhard K. H. Salje. "Multiferroic neuromorphic computation devices." APL Materials 12, no. 6 (2024). http://dx.doi.org/10.1063/5.0216849.

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Neuromorphic computation is based on memristors, which function equivalently to neurons in brain structures. These memristors can be made more efficient and tailored to neuromorphic devices by using ferroelastic domain boundaries as fast diffusion paths for ionic conduction, such as of oxygen, sodium, or lithium. In this paper, we show that the local memristor generates a second, unexpected feature, namely, weak magnetic fields that emerge from moving ferroelastic needle domains and vortices. The vortices appear near ferroelastic “junctions” that are common when the external stimulus is a comb

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Pati, Satya Prakash, and Takeaki Yajima. "Review of solid-state proton devices for neuromorphic information processing." Japanese Journal of Applied Physics, February 14, 2024. http://dx.doi.org/10.35848/1347-4065/ad297b.

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Abstract This is a review of proton devices for neuromorphic information processing. While solid-state devices utilizing various ions have been widely studied for non-volatile memory, the proton, which is the smallest ion, has been relatively overlooked despite its advantage of being able to move through various solids at room temperature. With this advantage, it should be possible to control proton kinetics not only for fast analog memory function, but also for real-time neuromorphic information processing in the same time scale as humans. Here, after briefing the neuromorphic concept and the

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Ju, Dongyeol, Jungwoo Lee, and Sungjun Kim. "Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching." Advanced Materials Technologies, May 19, 2024. http://dx.doi.org/10.1002/admt.202400440.

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AbstractIn the era of big data, traditional computing architectures face limitations in handling vast amounts of data owing to the separate processing and memory units, thus causing bottlenecks and high‐energy consumption. Inspired by the human brain's information exchange mechanism, neuromorphic computing offers a promising solution. Resistive random access memory devices, particularly those with bilayer structures like Pt/TaOx/TiOx/TiN, show potential for neuromorphic computing owing to their simple design, low‐power consumption, and compatibility with existing technology. This study investi

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Lin, Xiangde, Zhenyu Feng, Yao Xiong, et al. "Piezotronic Neuromorphic Devices: Principle, Manufacture, and Applications." International Journal of Extreme Manufacturing, March 13, 2024. http://dx.doi.org/10.1088/2631-7990/ad339b.

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Abstract With the arrival of the era of artificial intelligence (AI) and big data, the explosive growth of data has raised higher demands on computer hardware and systems. Neuromorphic techniques inspired by biological nervous systems are expected to be one of the approaches to break the von Neumann bottleneck. Piezotronic neuromorphic devices modulate electrical transport characteristics by piezopotential and directly associate external mechanical motion with electrical output signals in an active manner, with the capability to sense/store/process information of external stimuli. In this revi

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