Two-line neuristor with active element in series and in parallel†

Nishizawa, Jun–ichi; Hayasaka, A.

doi:10.1080/00207216908938173

Cited by 10 publications

(4 citation statements)

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“…9(f) shows various types of spiking behaviors achieved with different capacitance, indicating the versatility. It is worthwhile to point out that neuristors have been demonstrated previously using other voltage-controlled negative differential resistance devices such as tunnel junctions [180], [181]. These designs usually require nonscalable inductors to operate [180], [181], while Mott switches can be scaled down to nanometer regime.…”

Section: Neuromorphic Devices: Neuristorsmentioning

confidence: 99%

“…It is worthwhile to point out that neuristors have been demonstrated previously using other voltage-controlled negative differential resistance devices such as tunnel junctions [180], [181]. These designs usually require nonscalable inductors to operate [180], [181], while Mott switches can be scaled down to nanometer regime. Although the capacitors used were very large due to the large parasitic capacitance of the test apparatus, lowering the capacitance not only leads to reduced device area but also helps to generate higher frequency spikes and will be an important design criterion for more realistic circuits.…”

Section: Neuromorphic Devices: Neuristorsmentioning

confidence: 99%

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Mott Memory and Neuromorphic Devices

2015

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| Orbital occupancy control in correlated oxides allows the realization of new electronic phases and collective state switching under external stimuli. The resultant structural and electronic phase transitions provide an elegant way to encode, store, and process information. In this review, we examine the utilization of Mott metal-to-insulator transitions, for memory and neuromorphic devices. We emphasize the overarching electron-phonon coupling and electron-electron interaction-driven transition mechanisms and kinetics, which renders a general description of Mott memories from aspects such as nonvolatility, sensing scheme, read/write speed, and switching energy. Various memory and neuromorphic device architectures incorporating phase transition elements are reviewed, focusing on their operational principles. The role of Peierls distortions and crystal symmetry changes during phase change is discussed. Prospects for such orbitronic devices as hardware components for information technologies are summarized.

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Section: Neuromorphic Devices: Neuristorsmentioning

confidence: 99%

Section: Neuromorphic Devices: Neuristorsmentioning

confidence: 99%

Mott Memory and Neuromorphic Devices

2015

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“…Biological brains can perform computational tasks at an ∼100,000× efficiency compared to the digital computers. − A typical biological neuron has a surface area of ∼10 μm 2 , spends ∼10 pJ energy to generate each spike, and operates at a frequency of ∼100 Hz, which translates to a power cost of ∼1 nW for biological systems. ,, The first set of efforts in emulating biological neurons dates back to 1960s following the FN model using voltage controlled NDR devices , paired with inductors to produce relaxation oscillations similar to neuronal spiking behavior. − The inductor element is the main scaling bottleneck of this circuit implementation of spiking neuron, as coil-based passive inductors are difficult to fabricate at nanoscale with the required inductance values. The emergence of current-controlled NDR devices featuring metal–insulator phase transition materials has enabled generation of relaxation oscillations using capacitors, leading to considerable progress in artificial spiking neurons. − There have been other approaches to producing NDR, such as band to band tunneling, resonant tunneling, Gunn effect, real space electron transfer in III–V heterostructures, body biasing of MOSFET, exploiting graphene’s unique dispersion relationship near its Dirac point, using trap-based recombination processes, redox behavior of molecular junctions, and multiple circuits. ,,− Recently, we have shown that a graphene–silicon photodetector can show voltage-dependent NDR behavior under optical illumination while operating in the photovoltaic regime .…”

Section: Introductionmentioning

confidence: 99%

Ultralow Power Electronic Analog of a Biological Fitzhugh–Nagumo Neuron

Ahsan,

Wu,

Jalal

et al. 2024

ACS Omega

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Here, we introduce an electronic circuit that mimics the functionality of a biological spiking neuron following the Fitzhugh−Nagumo (FN) model. The circuit consists of a tunnel diode that exhibits negative differential resistance (NDR) and an active inductive element implemented by a single MOSFET. The FN neuron converts a DC voltage excitation into voltage spikes analogous to biological action potentials. We predict an energy cost of 2 aJ/cycle through detailed simulation and modeling for these FN neurons. Such an FN neuron is CMOS compatible and enables ultralow power oscillatory and spiking neural network hardware. We demonstrate that FN neurons can be used for oscillator-based computing in a coupled oscillator network to form an oscillator Ising machine (OIM) that can solve computationally hard NPcomplete max-cut problems while showing robustness toward process variations.

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“…This idea was initially proposed more than 50 years ago and got widespread popularity with the works of Carver Mead. [13][14][15][16] Since then, CMOS-based circuitry has been successfully used to implement neuromorphic systems, allowing tunable and efficient neural networks. [17][18][19] However, CMOS-based components rely on multiple transistors and capacitors, making them complex and extensive.…”

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confidence: 99%

Thermal Management in Neuromorphic Materials, Devices, and Networks

2023

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Machine learning has experienced unprecedented growth in recent years, often referred to as an “artificial intelligence revolution.” Biological systems inspire the fundamental approach for this new computing paradigm: using neural networks to classify large amounts of data into sorting categories. Current machine‐learning schemes implement simulated neurons and synapses on standard computers based on a von Neumann architecture. This approach is inefficient in energy consumption, and thermal management, motivating the search for hardware‐based systems that imitate the brain. Here, the present state of thermal management of neuromorphic computing technology and the challenges and opportunities of the energy‐efficient implementation of neuromorphic devices are considered. The main features of brain‐inspired computing and quantum materials for implementing neuromorphic devices are briefly described, the brain criticality and resistive switching‐based neuromorphic devices are discussed, the energy and electrical considerations for spiking‐based computation are presented, the fundamental features of the brain's thermal regulation are addressed, the physical mechanisms for thermal management and thermoelectric control of materials and neuromorphic devices are analyzed, and challenges and new avenues for implementing energy‐efficient computing are described.

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Two-line neuristor with active element in series and in parallel†

Cited by 10 publications

References 1 publication

Mott Memory and Neuromorphic Devices

Mott Memory and Neuromorphic Devices

Ultralow Power Electronic Analog of a Biological Fitzhugh–Nagumo Neuron

Thermal Management in Neuromorphic Materials, Devices, and Networks

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