Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Neuromorphic devices.
Статті в журналах з теми "Neuromorphic devices"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Neuromorphic devices".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
Ielmini, Daniele, and Stefano Ambrogio. "Emerging neuromorphic devices." Nanotechnology 31, no. 9 (2019): 092001. http://dx.doi.org/10.1088/1361-6528/ab554b.
Повний текст джерела
2
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.
Повний текст джерелаАнотація:
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 devices, as well as the basic framework and algorithms of neuromorphic computing. Then, the paper analyzes the advantages and difficulties of neuromorphic computing based on synaptic devices, including the preparation, testing, modelling and integration of the devices, as well as the systems architecture, programming and optimization. Then, this paper gives examples of the applications and effects of synaptic device-based neuromorphic computing in artificial intelligence fields such as image processing and natural language processing, including image denoising, image segmentation, image recognition, text classification, text summarization, and text generation. Finally, this paper summarizes the current research status and future synaptic device-based neuromorphic computing trends. It puts forward some research directions and suggestions to promote the development and innovation in this field.
3
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.
Повний текст джерелаАнотація:
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 explored across the full wavelength range (ultraviolet to infrared) by categorizing them on the basis of irradiated wavelength and structure (two-terminal and three-terminal) with respect to emerging optoelectrical materials. The relationship between neuromorphic applications, light wavelength, and mechanism is revisited. Finally, the potential and challenging aspects of next-generation optoelectronic neuromorphic devices are presented, which can assist in the design of suitable materials and structures for neuromorphic-based applications.
4
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.
Повний текст джерелаАнотація:
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 functions, their application in neuromorphic computing is still in its early stage. In this review, transistor-based synaptic devices are analyzed by categorizing them into different working mechanisms, and the device fabrication processes and synaptic properties are discussed. Future efforts that could be beneficial to the development of transistor-based synaptic devices in neuromorphic computing are proposed.
5
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.
Повний текст джерелаАнотація:
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 the metal. Additionally, numerous intermediate states could be implemented based on the number of cycles. Furthermore, each state persisted for 10,000 s after the gate pulses were removed, demonstrating excellent synaptic properties of the long-term memory. Moreover, the variation of drain current with cycle number demonstrates the device’s excellent linearity and symmetry for excitatory and inhibitory behaviors when prepared on a glass substrate intended for transparent devices. The results, therefore, suggest that such unique synaptic devices with extremely stable and superior properties could replace conventional semiconducting devices in the future.
6
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.
Повний текст джерелаАнотація:
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 heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.
7
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.
Повний текст джерелаАнотація:
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, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles.
8
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.
Повний текст джерела
9
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.
Повний текст джерелаАнотація:
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 circuit designs have been proposed for neuromorphic computing and applied to different proof-of-concept applications, there is still a long way to go to build large-scale low-power memristor-based neuromorphic systems that can bridge the gap between AI and biological brains. This Perspective summarizes the progress and challenges from memristor devices to neuromorphic systems and proposes possible directions for neuromorphic system implementation based on memristive devices.
10
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.
Повний текст джерелаАнотація:
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 weight update behavior of the synaptic element. However, it is challenging to obtain such a linear and symmetrical behavior with RRAM devices. Thus, extensive research is currently devoted at different levels, from material to device engineering, to improve the linearity and symmetry of the conductance update of RRAM devices. In this work, the experimental results based on different programming pulse conditions of RRAM devices are presented, considering both voltage and current pulses. Their suitability for application as analog RRAM-based synaptic devices for neuromorphic computing is analyzed by computing an asymmetric nonlinearity factor.
11
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.
Повний текст джерелаАнотація:
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 efficiency and high memory density. Though various emerging memory devices have been extensively studied to emulate the functionality of biological synapses, there is currently no material/device system that encompasses both the needed metrics for high-performance neuromorphic computing and the required biocompatibility for potential body-computer integration. In this review, we aim to equip the reader with general design principles and materials requirements for realizing high-performance organic neuromorphic devices. We use instructive examples from recent literature to discuss each requirement, illustrating the challenges as well as future research opportunities. Though organic devices still face many challenges to become major players in neuromorphic computing, mostly due to their lack of compliance with back-end-of-line processes required for integration with digital logic, we propose that their biocompatibility and mechanical conformability give them an advantage for creating adaptive biointerfaces, brain-machine interfaces, and biology-inspired prosthetics.
12
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.
Повний текст джерелаАнотація:
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 computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.
13
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.
Повний текст джерелаАнотація:
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 can these devices be used for compute-in-memory architectures to tackle the von Neumann bottleneck, the switching dynamics of the devices can also be used to directly process temporal data in a biofaithful fashion. In this review, we analyze the physical mechanisms that govern the dynamic switching behaviors and highlight how these properties can be utilized to efficiently implement synaptic and neuronal functions. Prototype systems that have been used in machine learning and brain-inspired network implementations will be covered, followed with discussions on the challenges for large scale implementations and opportunities for building bio-inspired, highly complex computing systems.
14
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.
Повний текст джерела
15
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.
Повний текст джерелаАнотація:
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 capable of learning and computing. Thus, artificial synapses which can emulate functionalities and the plasticity of bio-synapses form the backbones of neuromorphic computing. Alternative artificial synapses have been successfully demonstrated. The classical two-terminal memristor devices, like resistive random access memory (ReRAM), phase change memory (PCM) and ferroelectric tunnel junctions (FTJs) with one terminal connected to the pre-synaptic neuron and another connected with the post-synaptic neuron, own advantages of simple structure, easy processing with high density, and capability of integration with CMOS. However, signal processing and learning cannot be performed simultaneously in 2-terminal devices, thus limiting their synaptic functionalities. Ferroelectric field effect transistors (FeFET) which uses ferroelectric as the gate oxide are the most interesting three-terminal artificial synapse devices, in which the gate or the source is connected to the pre-synaptic neuron while the drain is used for the terminal of the post-synaptic neuron , thus can perform signal transmission and learning simultaneously. However, traps at the channel interface can degrade the device performance causing low endurance. Focuses of those abovementioned devices have been mainly put on the homosynaptic plasticity, which is input specific, meaning that the plasticity occurs only at the synapse with a pre-synaptic activation . The homosynaptic plasticity has a drawback of positive feedback loop: when a synapse is potentiated, the probability of the synapse to be further potentiated is increased. Similarly, when a synapse is depressed the probability of the synapse of being further depressed is higher. Therefore, synaptic weights tend to be either strengthened to the maximum value or weakened to zero, causing the system to be unstable. In contrast, heterosynaptic plasticity can be induced at any synapse at the same time after episodes of strong postsynaptic activity, avoiding the positive feedback problem and stabilize the activity of the post-synaptic neuron. To address the above challenges we proposed a very simple 4-terminal synapse structure based on gated Schottky diodes on silicon (FEMOD) with a ferroelectric layer. The conductance of the Schottky diode is modulated by the polarization of the ferroelectric layer. With this simple synapse structure we can achieve multiple hetero-synaptic functions, including excitatory/ inhibitory post-synaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), long-term potentiation/depression (LTP/LTD), as well as biological neuron-like spike-timing-dependent plasticity (STDP) characteristics. The modulatory synapse can modify the weight of another synapse with a very low voltage. Furthermore, logic gates, like AND and NAND which are highly desired for in-memory computing can be realized with such simple structure. Figure 1
16
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.
Повний текст джерела
17
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.
Повний текст джерелаАнотація:
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 for neuromorphic applications.
18
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.
Повний текст джерелаАнотація:
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-zinc-oxide-based photoelectric neuromorphic transistors are fabricated for Morse coding based on spike processing, 405-nm light spikes are used as synaptic inputs, and some essential synaptic plasticity, including excitatory postsynaptic current, short-term plasticity, and high-pass filtering, can be mimicked. More interestingly, Morse codes encoded by light spikes are decoded using our devices and translated into amplitudes. Furthermore, such devices are compatible with standard integrated processes suitable for large-scale integrated neuromorphic systems.
19
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.
Повний текст джерелаАнотація:
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 controls conductance changes, and trained a neural network to predict the handwritten digits from the standard database MNIST. Furthermore, we quantitatively considered the non-ideal case, to ensure reliability by limiting the conductance level to that which synaptic devices can practically accept. Based on this proposed learning method, we significantly improved the neuromorphic system, without any hardware modifications to the synaptic devices or neuromorphic systems. Thus, the results emphatically show that, even for devices with poor synaptic characteristics, the neuromorphic system performance can be improved.
20
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.
Повний текст джерелаАнотація:
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 is investigated systemically with a wide range of magnetic parameters, and the optimal values are presented accordingly. Finally, the functionalities of short-term plasticity and long-term potentiation are demonstrated via skyrmion excitation by a sequence of surface acoustic waves with different intervals. The application of surface acoustic waves in skyrmionic neuromorphic computing devices paves a novel approach to low-power computing systems.
21
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.
Повний текст джерелаАнотація:
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 essential to maximize their potential. In this paper, we propose a short-term wind power forecasting model based on spiking neural networks adapted to the computational abilities of Loihi, a neuromorphic device developed by Intel. A case study is presented with real wind power generation data from Ireland to evaluate the ability of the proposed approach, reaching a normalised mean absolute error of 2.84 percent for one-step-ahead wind power forecasts. The study illustrates the plausibility of the development of neuromorphic devices aligned with the specific demands of the wind energy sector.
22
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.
Повний текст джерелаАнотація:
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 devices show such characteristics, the inherent error by their physical properties needs to be resolved. This paper suggests adopting an approximate computing approach to deal with the error without degrading the advantages of emerging memory devices.
23
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.
Повний текст джерелаАнотація:
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 multiply accumulate (MAC) operations. Here the conductance of memory devices in a crossbar array represents DNN weights and the activations are encoded in input electrical signals (e.g., pulse height or duration). The MAC operation is conducted via the Ohm’s law (multiplication between voltage and conductance) and Kirchhoff’s law (accumulate via current summation) at constant time even for very large networks. DNN has surpassed human performance in various AI applications, e.g., image classification, natural language processing, etc. While general-purpose CPU/GPU and special-purpose digital accelerators provide current and near-term DNN hardware, there are longer-term opportunities for analog DNN accelerators based on emerging memory devices to achieve significantly higher performance and energy-efficiency. At the same time, analog accelerators impose new requirements on these devices beyond traditional memory applications, e.g., analog tunability, gradual and symmetric weight modulation, high precision, etc. Memory devices with analog nature in their physical mechanisms (e.g., filament growth in RRAM) may be optimized to meet these requirements, while some abrupt and asymmetric characteristics (e.g., filament rupture) present challenges. Increasingly large neural network models have been demonstrated on these memory arrays designed as analog accelerators, but they are still orders of magnitude smaller than state-of-the-art DNN models. While analog accelerators enable massively parallel computation, they are also susceptible to unique challenges in analog devices and circuitry (e.g., device variability, circuit noise), which may degrade network performance (e.g., accuracy). To benefit from the massively parallel MAC operation in analog memory arrays, these arrays need to be large enough to efficiently map the layers in modern DNN models. Among emerging NVM devices, PCM has the advantages of maturity and the availability of large-scale arrays, but also face some challenges in device characteristics, e.g., conductance drift, asymmetry, and noise. PCM-based analog DNN accelerators have been demonstrated at advanced technology node with millions of devices and achieved iso-accuracy on increasingly large network models. These accelerators integrate highly efficient analog PCM tiles for MAC operations with advanced CMOS circuitry for auxiliary digital functions. While material/device engineering continues to be explored to improve the analog properties of PCM devices, design and operation innovations can also help to improve the performance of PCM-based DNN weights, e.g., multiple-device-per-weight design, close-loop tunning. In addition, circuit innovations are essential for analog accelerator performance. Fig. 1 shows a 14nm PCM-based DNN inference accelerator, which incorporate design techniques such as 4-PCM weight units, 2D mesh for tile-to-tile communication, pulse-duration-based coding, etc. On top of technology and design innovations, some DNN models can also be modified to be more resilient against hardware imperfection and noise. PCM-based analog accelerators have achieved iso-accuracy on large DNN models with millions of weights. This talk will discuss the progress that we have achieved on PCM-based analog DNN inference accelerators, the challenges of PCM materials and devices, and promising solutions in technology and design. Figure 1
24
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.
Повний текст джерела
25
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.
Повний текст джерелаАнотація:
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 circuit simulation with MOSFET models to study how device-level imperfections can make adversarial effects on the analog circuits that implement neuromorphic analog signal processing. Methods on memristor stamping in the framework of modified nodal analysis formulation are presented, and implementation results are reported. Furthermore, functional simulations on neuromorphic signal processing circuits including memristors and CMOS devices are carried out to validate the effectiveness of the native SPICE implementation of memristor models from the perspectives of simulation accuracy, efficiency, and convergence for large-scale simulation tasks.
26
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.
Повний текст джерелаАнотація:
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 exploitation of material defects. Applications in artificial intelligence, edge computing, sensor networks, and robotics are highlighted, showcasing the potential of 2D materials to revolutionize these fields. The paper also addresses the challenges related to scalability, uniformity, and energy efficiency, and concludes by offering perspectives on future research directions in this burgeoning field. This comprehensive study underscores the significance of 2D materials in advancing neuromorphic computing, paving the way for more efficient, powerful, and brain-like artificial intelligence systems.
27
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.
Повний текст джерелаАнотація:
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 resistance state (HRS) to low resistance state (LRS) and RESET from LRS to HRS by voltage pulses without compliance current. With Ag and Cu top electrodes, respectively, eight and six levels of resistance switching were demonstrated in the SnSe/SrTiO3 heterostructures with a Pt bottom electrode. These results suggest that a SnSe/STO heterojunction-based memristor is promising for applications in neuromorphic computing as a synaptic device.
28
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.
Повний текст джерела
29
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.
Повний текст джерела
30
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.
Повний текст джерела
31
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.
Повний текст джерела
32
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.
Повний текст джерела
33
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.
Повний текст джерела
34
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.
Повний текст джерелаАнотація:
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 devices have demonstrated a range of neuromorphic functions such as multistate storage, spike-timing-dependent plasticity, and dynamic filtering, etc. To achieve high performance neuromorphic computing systems, it is essential to fabricate neuromorphic devices compatible with complementary metal oxide semiconductor (CMOS) manufacturing process. This improves the device reliability and stability and is favourable to achieve neuromorphic chips with higher integration density and low power consumption. This review summarizes CMOS-compatible neuromorphic devices and discusses their emulation of synaptic and neuronal functions as well as their applications in neuromorphic perception and computing. We highlight challenges and opportunities for further development of CMOS-compatible neuromorphic devices and systems.
35
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.
Повний текст джерелаАнотація:
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 multifunctional applications. The synthesis and next micro-nano fabrication methods of 2D materials and their heterostructures are first introduced. The recent advances of neuromorphic 2D devices are discussed in details using different operating principles. More importantly, we present a review of emerging multifunctional neuromorphic applications, including neuromorphic visual, auditory, tactile, and nociceptive systems based on 2D devices. In the end, we discuss the problems and methods for 2D neuromorphic device developments in the future. This paper will give insights into designing 2D neuromorphic devices and applying them to the future neuromorphic systems.
36
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.
Повний текст джерелаАнотація:
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 neuromorphic computing, neuromorphic devices are very critical in building neuromorphic chips. Meanwhile, the development of human visual systems and optogenetics also provide new insights into how to carry out the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices feature the unique advantages of photonics and electronics, showing great potential in the neuromorphic computing field and attracting more and more attention of the scientists at at home and abroad. In view of these, the main purpose of this review is to disclose the recent advances of optoelectronic neuromorphic devices and the prospects of their practical applications. We first review the artificial optoelectronic synapses and neurons, including device structural features, working mechanism principles, neuromorphic simulation functions, and so on. Then, we exhibit and introduce the applications of optoelectronic neuromorphic devices particularly suitable for the fields involving artificial vision systems, artificial perception systems, neuromorphic computing etc. At last, we summarize the challenges the optoelectronic neuromorphic devices are currently facing, and disclose some perspectives about their development directions in the future.
37
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.
Повний текст джерелаАнотація:
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 resistance manipulation is also established in both DC and pulse modes. Moreover, we successfully realize the decimal operation function of numbers by applying pulse stimulation to the device to regulate the device conductance. Our work demonstrates the feasibility of IMHP in neuromorphic devices, accelerating the application of neuromorphic computing.This article is protected by copyright. All rights reserved.
38
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.
Повний текст джерелаАнотація:
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 properties, such as resistive random-access memories, ferroelectric random-access memories, ferroelectric tunnel junctions, and (anti)ferroelectric field-effect transistors. We also discuss the potential of these devices for mimicking synaptic and neuronal functions and address the challenges and future research directions. Hafnia-based neuromorphic devices promise breakthrough performance improvements through material optimization, such as crystallization engineering and innovative device configuration designs, paving the way for advanced artificial intelligence systems.
39
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.
Повний текст джерелаАнотація:
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 to create a wide range of soft devices and systems with implemented neuromorphic and cognitive functions, including artificial cognitive skins, wearable neuromorphic computing, artificial organs, neurorobotics, and skin prosthetics.
40
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.
Повний текст джерелаАнотація:
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 devices for next-generation computing. In this review, we summarize the 2D materials and their heterostructures to be used for neuromorphic computing devices, which could be classified by the working mechanism and device geometry. Then, we survey neuromorphic device arrays and their applications including artificial visual, tactile, and auditory functions. Finally, we discuss the current challenges of 2D materials to achieve practical neuromorphic devices, providing a perspective on the improved device performance, and integration level of the system. This will deepen our understanding of 2D materials and their heterojunctions and provide a guide to design highly performing memristors. At the same time, the challenges encountered in the industry are discussed, which provides a guide for the development direction of memristors.
41
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.
Повний текст джерелаАнотація:
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 are examined, with an emphasis on their structural characteristics, operational principles, and the replication of neuromorphic functions. For optoelectronic synaptic devices, such as memristor‐ and transistor‐based ones, attention is given to the two primary weight update modes: the light‐electricity synergistic mode and the all‐optical mode. Optoelectronic neurons are discussed in terms of different device types, including threshold switch neurons and semiconductor laser neurons. Last, the challenges that impede the progress of optoelectronic neuromorphic devices are identified, and potential future directions are suggested.
42
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.
Повний текст джерелаАнотація:
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, limited scalability in integrated circuits, and non-linearity in weight change. To address these challenges, alternative approaches are required. In this study, we introduce a multi-layer-multi-terminal neuromorphic computing device based on the asymmetric temperature gradient. Our device exhibits a wide range of synaptic functions, including potentiation, depression, and both anti-symmetric and symmetric spike-timing-dependent plasticity. The thermal driving strategy offers an energy-efficient platform for future neuromorphic computing devices to achieve artificial intelligence.
43
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.
Повний текст джерелаАнотація:
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. Herein, several fascinating strategies for synaptic plasticity modulation through chemical techniques, device structure design, and physical signal sensing are reviewed. For chemical techniques, the underlying mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted. Based on device structure design, the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions. Besides, integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light, strain, and temperature. Finally, considering that the relevant technology is still in the basic exploration stage, some prospects or development suggestions are put forward to promote the development of neuromorphic devices.
44
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.
Повний текст джерелаАнотація:
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 introducing the potential of the space charge to couple gate potential, photoexcitation, and photoinhibition occur successively in the channel under the interference of constant light intensity, which enables the device to transform from a linear model to a nonlinear model. As a result, the device exhibits a dynamic range of over 100 dB, exceeding the currently reported similar neuromorphic synaptic devices. Further, the device achieves adaptive tone mapping within 5 s for static information and achieves over 90% robustness recognition accuracy for dynamic information. Therefore, this work provides a new strategy for developing advanced neuromorphic devices and has great potential in the fields of intelligent driving and brain‐like computing.
45
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.
Повний текст джерелаАнотація:
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 their potential for enhancing neuromorphic computing capabilities. Together with device performance, it is crucial to select devices that allow fabrication via scalable techniques. This study investigates the fabrication of OMIEC‐based neuromorphic devices using inkjet printing, providing a scalable and material‐efficient approach. Employing a commercially available polymer mixed ionic‐electronic conductor (BTEM‐PPV) and a lithium salt, inkjet‐printed devices exhibit performance comparable to those fabricated via traditional spin‐coating methods. These two‐terminal neuromorphic devices demonstrate functionality analogous to literature‐known devices and demonstrate promising frequency‐dependent short‐term plasticity. Furthermore, comparative studies with previous light‐emitting electrochemical cells (LECs) and neuromorphic OMIEC devices validate the efficacy of inkjet printing as a potential fabrication technique. The findings suggest that inkjet printing is suitable for large‐scale production, offering reproducible and stable fabrication processes. By adopting the OMIEC material system, inkjet printing holds the potential for further enhancing device performance and functionality. Overall, this study underscores the viability of inkjet printing as a scalable fabrication method for OMIEC‐based neuromorphic devices, paving the way for advancements in AI hardware.
46
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.
Повний текст джерелаАнотація:
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-channel signals. However, the traditional rigid neuromorphic transistor is difficult to achieve bending deformation and close fit with the human body, which limits the application range of neuromorphic devices. Therefore, the research of flexible neuromorphic transistor with good bending characteristics has become the focus of recent research. Firstly, this review introduces the research progress of many kinds of flexible neuromorphic transistors, including device structure, working principle and basic functions. In addition, the application of the flexible neuromorphic transistor in the field of bionic perception is also introduced. Finally, this review also gives a summary and simple prospect of the above research fields.
47
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.
Повний текст джерелаАнотація:
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 combination of electric fields and structural phase transitions. Many ferroelastic materials show such phase transitions near room temperatures so that device applications display a “multiferroic” scenario where the memristor is driven electrically and read magnetically. Our computer simulation study of an elastic spring model suggests magnetic fields in the order of 10−7 T, which opens the way for a fundamentally new way of running neuromorphic devices. The magnetism in such devices emerges entirely from intrinsic displacement currents and not from any intrinsic magnetism of the material.
48
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.
Повний текст джерелаАнотація:
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 basic proton behavior in solid-state devices, we review the proton devices that have been reported so far, classifying them according to their device structures. The benchmark clearly shows the time scales of proton relaxation ranges from several milliseconds to hundreds of seconds, and completely match the time scales for expected neuromorphic functions. The incorporation of proton degrees of freedom in electronic devices will also facilitate access to electrochemical phenomena and subsequent phase transitions, showing great promise for neuromorphic information processing in the real-time and highly interactive edge devices.
49
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.
Повний текст джерелаАнотація:
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 investigates the synaptic applications of Pt/TaOx/TiOx/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaOx/TiOx/TiN device enables the realization of reservoir computing and the functions of artificial nociceptors and synapses. Additionally, the linkage between artificial nociceptors and synapses is examined based on injury‐enhanced spike‐time‐dependent plasticity paradigms. This study underscores the Pt/TaOx/TiOx/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles.
50
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.
Повний текст джерелаАнотація:
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 review, we have presented the piezotronic neuromorphic devices (which are classified into strain-gated piezotronic transistors and piezoelectric nanogenerator (PENG)-gated field effect transistors based on device structure) and discussed their operating mechanisms and related manufacture techniques. Secondly, we summarize the research progress of piezotronic neuromorphic devices in recent years and provide a detailed discussion on multifunctional applications including bionic sensing, information storage, logic computing, and electrical/optical artificial synapses. Finally, in the context of future development, challenges, and perspectives, we have discussed how to more effectively modulate novel neuromorphic devices with piezotronic effects. It is believed that the piezotronic neuromorphic devices have great potential for the next generation of interactive sensation/memory/computation to facilitate the development of the Internet of Things, AI, biomedical engineering, etc.