Mimicry of Excitatory and Inhibitory Artificial Neuron With Leaky Integrate-and-Fire Function by a Single MOSFET

Han, Joon‐Kyu; Choi, Yang‐Kyu; Seo, Myungsoo; Kim, Wu‐Kang; Kim, Moon-Seok; Kim, Seongyeon; Kim, Myung‐Su; Yun, Gyeong‐Jun; Lee, Geon‐Beom; Yu, Ji‐Man

doi:10.1109/led.2019.2958623

Cited by 72 publications

(47 citation statements)

References 22 publications

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“…Afterward, it is sustained in a floating state for the neuron operation. Because of nonvolatility of the trapped charges even without gate biasing, energy consumption is much smaller compared to our previous study, which required additional and continuous gate voltage control (33,34).…”

Section: Unit Device Characteristics Of Neuron and Synapsementioning

confidence: 73%

Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware

Han

Yun

et al. 2021

Sci. Adv.

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Cointegration of multistate single-transistor neurons and synapses was demonstrated for highly scalable neuromorphic hardware, using nanoscale complementary metal-oxide semiconductor (CMOS) fabrication. The neurons and synapses were integrated on the same plane with the same process because they have the same structure of a metal-oxide semiconductor field-effect transistor with different functions such as homotype. By virtue of 100% CMOS compatibility, it was also realized to cointegrate the neurons and synapses with additional CMOS circuits. Such cointegration can enhance packing density, reduce chip cost, and simplify fabrication procedures. The multistate single-transistor neuron that can control neuronal inhibition and the firing threshold voltage was achieved for an energy-efficient and reliable neural network. Spatiotemporal neuronal functionalities are demonstrated with fabricated single-transistor neurons and synapses. Image processing for letter pattern recognition and face image recognition is performed using experimental-based neuromorphic simulation.

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Section: Unit Device Characteristics Of Neuron and Synapsementioning

confidence: 73%

Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware

Han

Yun

et al. 2021

Sci. Adv.

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“…Note that processing these signals simultaneously can reduce memory usage and simplify the peripheral circuitry. To alleviate these hardware burdens, memristor-based [14][15][16] and FET-based neuron devices [18][19][20][21][22][23] with memory functionalities have been studied to mimic neurons. Memristor-based neuron devices with two terminals replace membrane capacitors in neuron circuits and have the advantage of high density over membrane capacitor and FET-based neuronal devices.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, memristor-based neuron devices require additional circuits such as a differential amplifier to compare the resistance of the memristor to a reference resistance and a circuit so as to reset the memristor after the integrate-andfire operation. On the other hand, FET-based neuron devices, capable of resolving these issues, can process signals from two types (G + and G -) of synapses sequentially or only one type of signals [18][19][20][21][22][23]. However, FET-based neuron devices processing only one type of signals are paired for excitatory and inhibitory signals, and require additional circuits for logic operation in the neural networks.…”

Section: Introductionmentioning

confidence: 99%

Low-Power and High-Density Neuron Device for Simultaneous Processing of Excitatory and Inhibitory Signals in Neuromorphic Systems

et al. 2020

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A positive-feedback (PF) neuron device capable of threshold tuning and simultaneously processing excitatory (G +) and inhibitory (G-) signals is experimentally demonstrated to replace conventional neuron circuits, for the first time. Thanks to the PF operation, the PF neuron device with steep switching characteristics can implement integrate-and-fire (IF) function of neurons with low-energy consumption. The structure of the PF neuron device efficiently merges a gated PNPN diode and a single MOSFET. Integrateand-fire (IF) operation with steep subthreshold swing (SS < 1 mV/dec) is experimentally implemented by carriers accumulated in an n floating body of the PF neuron device. The carriers accumulated in the n floating body are discharged by an inhibitory signal applied to the merged FET. Moreover, the threshold voltage (Vth) of the proposed PF neuron is controlled by using a charge storage layer. The low-energy consuming PF neuron circuit (~ 0.62 pJ/spike) consists of one PF device and only five MOSFETs for the IF and reset operation. In a high-level system simulation, a deep-spiking neural network (D-SNN) based on PF neurons with four hidden layers (1024 neurons in each layer) shows high-accuracy (98.55%) during a MNIST classification task. The PF neuron device provides a viable solution for high-density and low-energy neuromorphic systems.

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“…[ 25–29 ] The biristor neuron is considered a possible candidate to replace bulky and high‐energy‐consuming CMOS circuit‐based spiking neurons for SNNs. [ 30–32 ] After analyzing the characteristics of the TENG and the biristor neuron, a self‐powered artificial mechanoreceptor module capable of detection near 3 kPa that is able to sense a gentle touch such as, handwriting and breathing is realized by connecting the two abovementioned components. Based on the spiking characteristics according to the pressure, classification of handwritten digits in the Modified National Institute of Standards and Technology (MNIST) dataset is performed with the aid of a software simulation in order to demonstrate that complex pattern recognition is possible with the neuromorphic tactile system composed of an artificial mechanoreceptor module.…”

Section: Introductionmentioning

confidence: 99%

Self‐Powered Artificial Mechanoreceptor Based on Triboelectrification for a Neuromorphic Tactile System

et al. 2022

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A self‐powered artificial mechanoreceptor module is demonstrated with a triboelectric nanogenerator (TENG) as a pressure sensor with sustainable energy harvesting and a biristor as a neuron. By mimicking a biological mechanoreceptor, it simultaneously detects the pressure and encodes spike signals to act as an input neuron of a spiking neural network (SNN). A self‐powered neuromorphic tactile system composed of artificial mechanoreceptor modules with an energy harvester can greatly reduce the power consumption compared to the conventional tactile system based on von Neumann computing, as the artificial mechanoreceptor module itself does not demand an external energy source and information is transmitted with spikes in a SNN. In addition, the system can detect low pressures near 3 kPa due to the high output range of the TENG. It therefore can be advantageously applied to robotics, prosthetics, and medical and healthcare devices, which demand low energy consumption and low‐pressure detection levels. For practical applications of the neuromorphic tactile system, classification of handwritten digits is demonstrated with a software‐based simulation. Furthermore, a fully hardware‐based breath‐monitoring system is implemented using artificial mechanoreceptor modules capable of detecting wind pressure of exhalation in the case of pulmonary respiration and bending pressure in the case of abdominal breathing.

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Mimicry of Excitatory and Inhibitory Artificial Neuron With Leaky Integrate-and-Fire Function by a Single MOSFET

Cited by 72 publications

References 22 publications

Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware

Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware

Low-Power and High-Density Neuron Device for Simultaneous Processing of Excitatory and Inhibitory Signals in Neuromorphic Systems

Self‐Powered Artificial Mechanoreceptor Based on Triboelectrification for a Neuromorphic Tactile System

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