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Bibliographies thématiques / Neuromorphic devices

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Littérature scientifique sur le sujet « Neuromorphic devices »

Auteur : Grafiati

Publié le 14 décembre 2024

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Articles de revues sur le sujet "Neuromorphic devices"

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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