- 标题
- 摘要
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- 实验方案
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[IEEE 2019 Workshop on Recent Advances in Photonics (WRAP) - Guwahati, India (2019.12.13-2019.12.14)] 2019 Workshop on Recent Advances in Photonics (WRAP) - Self-Focusing of Quadruple Gaussian Laser Beam in Relativistic Plasma using Moment Theory Approach
摘要： Phase-change materials and devices have received much attention as a potential route to the realization of various types of unconventional computing paradigms. In this letter, we present non-von Neumann arithmetic processing that exploits the accumulative property of phase-change memory (PCM) cells. Using PCM cells with integrated FET access devices, we perform a detailed study of accumulation-based computation. We also demonstrate efficient factorization using PCM cells, a technique that could pave the way for massively parallelized computations.
关键词： neuromorphic computing,Phase-change materials,non-von Neumann,arithmetic computing
更新于2025-09-19 17:13:59
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Solar-stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing
摘要： We report an artificial optoelectronic synapse based on a copper-phthalocyanine (CuPc) and para-sexiphenyl (p-6P) heterojunction structure. This device features stable conductance states and their linear distribution in long-term potentiation (LTP) characteristic curve formed by continuous input light pulses. These superior synaptic characteristics originate from the fact that the number of photo-holes moving into the CuPc channel and photo-electrons being trapped at the p-6P/dielectric interface is constant at every light pulse. A single-layer neural network is theoretically formed with these optoelectronic synaptic devices and its feasibility is studied in terms of training/recognition tasks of the Modified National Institute of Standards and Technology digit image patterns. Owing to the excellent LTP characteristic and through the use of a unidirectional update method, its maximum recognition rate is as high as 78% despite the use of a single-layer network. This study is expected to provide a foundation for future studies on optoelectronic synaptic devices toward the implementation of complex artificial neural networks.
关键词： Solar-stimulated optoelectronic synapse,Neuromorphic computing,Band engineering,Pattern recognition,Organic heterojunction
更新于2025-09-19 17:13:59
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2D photonic memristor beyond graphene: progress and prospects
摘要： Photonic computing and neuromorphic computing are attracting tremendous interests in breaking the memory wall of traditional von Neumann architecture. Photonic memristors equipped with light sensing, data storage, and information processing capabilities are important building blocks of optical neural network. In the recent years, two-dimensional materials (2DMs) have been widely investigated for photonic memristor applications, which offer additional advantages in geometry scaling and distinct applications in terms of wide detectable spectrum range and abundant structural designs. Herein, the recent progress made toward the exploitation of 2DMs beyond graphene for photonic memristors applications are reviewed, as well as their application in photonic synapse and pattern recognition. Different materials and device structures are discussed in terms of their light tuneable memory behavior and underlying resistive switching mechanism. Following the discussion and classification on the device performances and mechanisms, the challenges facing this rapidly progressing research field are discussed, and routes to realize commercially viable 2DMs photonic memristors are proposed.
关键词： neuromorphic computing,photonic synapse,photonic memristor
更新于2025-09-19 17:13:59
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[IEEE 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Sydney, Australia (2018.11.10-2018.11.17)] 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) - Laser-Based Scintillator Crystal Emulator for Optical Testing of SiPM Readout Technologies
摘要： Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms.
关键词： winner-take-all,probabilistic learning,stochastic computing,Neuromorphic systems,probabilistic inference,spiking neurons,stochastic memristors
更新于2025-09-19 17:13:59
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Molybdenum Disulfide Nanosheet/Quantum Dot Dynamic Memristive Structure Driven by Photoinduced Phase Transition
摘要： MoS2 2D nanosheets (NS) with intercalated 0D quantum dots (QDs) represent promising structures for creating low-dimensional (LD) resistive memory devices. Nonvolatile memristors based 2D materials demonstrate low power consumption and ultrahigh density. Here, the observation of a photoinduced phase transition in the 2D NS/0D QDs MoS2 structure providing dynamic resistive memory is reported. The resistive switching of the MoS2 NS/QD structure is observed in an electric field and can be controlled through local QD excitations. Photoexcitation of the LD structure at different laser power densities leads to a reversible MoS2 2H-1T phase transition and demonstrates the potential of the LD structure for implementing a new dynamic ultrafast photoresistive memory. The dynamic LD photomemristive structure is attractive for real-time pattern recognition and photoconfiguration of artificial neural networks in a wide spectral range of sensitivity provided by QDs.
关键词： neuromorphic computing,photoinduced phase transition,2D crystals and QDs,dynamic photomemristors,liquid phase exfoliation
更新于2025-09-19 17:13:59
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Origin of Current‐Controlled Negative Differential Resistance Modes and the Emergence of Composite Characteristics with High Complexity
摘要： Current-controlled negative differential resistance has significant potential as a fundamental building block in brain-inspired neuromorphic computing. However, achieving the desired negative differential resistance characteristics, which is crucial for practical implementation, remains challenging due to a lack of consensus on the underlying mechanism and design criteria. Here, a material-independent model of current-controlled negative differential resistance is reported to explain a broad range of characteristics, including the origin of the discontinuous snap-back response observed in many transition metal oxides. This is achieved by explicitly accounting for a non-uniform current distribution in the oxide film and its impact on the effective circuit of the device rather than a material-specific phase transition. The predictions of the model are then compared with experimental observations to show that the continuous S-type and discontinuous snap-back characteristics serve as fundamental building blocks for composite behavior with higher complexity. Finally, the potential of our approach is demonstrated for predicting and engineering unconventional compound behavior with novel functionality for emerging electronic and neuromorphic computing applications.
关键词： negative differential resistance,neuromorphic computing,threshold switching,nonlinear transport,nanoelectronics
更新于2025-09-19 17:13:59
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LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System
摘要： Dealing with big data, especially the videos and images, is the biggest challenge of existing Von-Neumann machines while the human brain, benefiting from its massive parallel structure, is capable of processing the images and videos in a fraction of second. The most promising solution, which has been recently researched widely, is brain-inspired computers, so-called neuromorphic computing systems (NCS). The NCS overcomes the limitation of the word-at-a-time thinking of conventional computers benefiting from massive parallelism for data processing, similar to the brain. Recently, spintronic-based NCSs have shown the potential of implementation of low-power high-density NCSs, where neurons are implemented using magnetic tunnel junctions (MTJs) or spin torque nano-oscillators (STNOs) and memristors are used to mimic synaptic functionality. Although using STNOs as neuron requires lower energy in comparison to the MTJs, still there is a huge gap between the power consumption of spintronic-based NCSs and the brain due to high bias current needed for starting the oscillation with a detectable output power. In this manuscript, we propose a spintronic-based NCS (196 × 10) proof-of-concept where the power consumption of the NCS is reduced by assisting the STNO oscillation through a microwatt nanosecond laser pulse. The experimental results show the power consumption of the STNOs in the designed NCS is reduced by 55.3% by heating up the STNOs to 100?C. Moreover, the average power consumption of spintronic layer (STNOs and memristor array) is decreased by 54.9% at 100?C compared with room temperature. The total power consumption of the proposed laser assisted STNO-based NCS (LAO-NCS) at 100?C is improved by 40% in comparison to a typical STNO-based NCS at room temperature. Finally, the energy consumption of the LAO-NCA at 100?C is expected to reduce by 86% compared with a typical STNO-based NCS at the room temperature.
关键词： COMSOL multiphysics,power efficient,laser,spin torque nano-oscillators,neuromorphic computing system
更新于2025-09-19 17:13:59
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[IEEE 2019 Device Research Conference (DRC) - Ann Arbor, MI, USA (2019.6.23-2019.6.26)] 2019 Device Research Conference (DRC) - Waveguide Uni-Traveling-Carrier Photodiodes for mmW Signal Generation: Space-Charge Impedance and Efficiency Limitations
摘要： Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms.
关键词： Neuromorphic systems,winner-take-all,stochastic computing,probabilistic learning,probabilistic inference,spiking neurons,stochastic memristors
更新于2025-09-19 17:13:59
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[IEEE 2019 International Vacuum Electronics Conference (IVEC) - Busan, Korea (South) (2019.4.28-2019.5.1)] 2019 International Vacuum Electronics Conference (IVEC) - Notice of Removal: Oversized Smooth-Walled Horn Feeder for High Power Millimeter Wave
摘要： Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms.
关键词： winner-take-all,probabilistic learning,stochastic computing,Neuromorphic systems,probabilistic inference,spiking neurons,stochastic memristors
更新于2025-09-16 10:30:52
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Dual Modes Electronic Synapse Based on Layered SnSe Films Fabricated by Pulsed Laser Deposition
摘要： Artificial synapse, such as memristive electronic synapse, caught world-wide attention, attributed to its potential in neuromorphic computing which may tremendously reduce the computer volume and energy consumption. Introducing of layered two-dimentional materials has been reported enhance performance of the memristive electronic synapse. However, it is still a challenge to fabricate large-area layered two-dimentioanl films by scalable methods, which has greatly limit the industrial application potential of two-dimentioanl materials. In this work, a scalable pulsed laser deposition (PLD) method has been ultilized to fabricate large-area layered SnSe films, which is used as the functional layer of memristive electronic synapse with dual modes. Both long-term memristive behaviour with gradually changed resistance (Mode 1) and short-term memristive behavior with abruptly reduced resistance (Mode 2) have been ahieved in this SnSe based memristive electronic synapse . The switching between Mode 1 and Mode 2 can be realized by series of voltage sweeping and programed pulses. Formation and recovery of Sn vacancies were believed to induce the short-term memristive behaviour, and the joint action of Ag filament formation/rupture and Schottky barrier modulation should be the origin of long-term memristive behaviour. DFT calculation was performed to further illustrate how Ag atoms and Sn vacancies diffuse through SnSe layer and form filaments. The successful emulation of synaptic functions by layered chalcogenides memristor fabricated by PLD method suggestes the application potential in future neuromorphic computers.
关键词： neuromorphic computing,layered two-dimentional materials,pulsed laser deposition,SnSe films,memristive electronic synapse,Artificial synapse,dual modes
更新于2025-09-16 10:30:52