Integrate-and-Fire Neuron Circuit Without External Bias Voltages

Park, Young Soo; Woo, Sola; Lim, Doohyeok; Cho, Kyoungah

doi:10.3389/fnins.2021.644604

Cited by 13 publications

(10 citation statements)

References 20 publications

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“…The circuitry is complex with 21 transistors and several capacitors that renders the manufacturing difficult and generates chip-level heat dissipation issues. To reduce the components used in the silicon neuron, an IF neuron circuit using a p-n-p-n diode (Park et al, 2021) was proposed (Figure 2B). Herein, the neuron circuit features temporal integration, refractory period, and tunable output spike frequency.…”

Section: Conventional Silicon Artificial Neuronsmentioning

confidence: 99%

“…The discussion includes: (i) filament-based neuron, (ii) ferroelectric neuron, (iii) spintronic (Indiveri et al, 2011). (B) Integrate-and-fire (IF) neuron circuit using a p-n-p-n diode (Park et al, 2021).…”

Section: Emerging San Devicesmentioning

confidence: 99%

“…FIGURE 2 | (A)Leaky integrate-and-fire (LIF) neuron circuit based on complementary metal-oxide-semiconductor (CMOS) technology(Indiveri et al, 2011). (B) Integrate-and-fire (IF) neuron circuit using a p-n-p-n diode(Park et al, 2021).…”

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confidence: 99%

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Emerging Artificial Neuron Devices for Probabilistic Computing

Geng

Wang

et al. 2021

Front. Neurosci.

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In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.

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Section: Conventional Silicon Artificial Neuronsmentioning

confidence: 99%

Section: Emerging San Devicesmentioning

confidence: 99%

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Emerging Artificial Neuron Devices for Probabilistic Computing

Geng

Wang

et al. 2021

Front. Neurosci.

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“…Park et al have recently reported a comprehensive overview of the state of the art when it comes to compact neuron designs [9]. Designs using novel electronic devices in general use less components and are more energy efficient, down to six transistors and 100 pJ/pulse.…”

Section: Introductionmentioning

confidence: 99%

A spiking neuron implemented in VLSI

Stoliar¹,

Akita²,

Schneegans³

et al. 2022

J. Phys. Commun.

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A VLSI implementation of a Silicon-Controlled Rectifier (SCR)-based Neuron that has the functionality of the leaky-integrate and fire model (LIF) of spiking neurons is introduced. The silicon-controlled rectifier is not straightforward to efficiently migrate to VLSI. Therefore, we propose a MOS transistor-based circuit that provides the same functionality as the SCR. The results of this work are based on Spice simulation using open libraries and on VLSI layout and post layout simulations for a 65nm CMOS process.

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“…Consequently, electrons flow toward the p + drain, and the accumulation of electrons prompts the injection of holes. More recently, their neuronal oscillation and spiking behaviors have opened their potential applications in neuron devices [7,8]. In order to use FBFETs in next-generation electronics, it is essential to examine their reliabilities under various conditions, including temperature and gate-bias stresses.…”

Section: Introductionmentioning

confidence: 99%

Electrical Stability of p-Channel Feedback Field-Effect Transistors Under Bias Stresses

Son

Cho

Kim³

2021

IEEE Access

Self Cite

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In this study, we examined electrical stability of the p-channel feedback field-effect transistors (FBFETs) under negative bias stress (NBS) and positive bias stress (PBS). The intact FBFETs have a subthreshold swing (SS) of 0.12 mV/dec, an on-current of ~10 -4 A, and a threshold voltage (V TH ) of -0.76 V. There is a negligible change in the on-current and SS when the FBFETs are stressed by a gate-bias voltage corresponding to an electric field of 5.4 MV/cm across the gate oxide. On the other hand, as the duration of the stress increases to 1000 s, the V TH shifts to -0.89 V and -0.67 V for NBS and PBS, respectively. The V TH was recovered to over 83% at a recovery bias voltage of 5 V. The electrical stabilities of FBFETs under NBS and PBS are discussed in this study.

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Integrate-and-Fire Neuron Circuit Without External Bias Voltages

Cited by 13 publications

References 20 publications

Emerging Artificial Neuron Devices for Probabilistic Computing

Emerging Artificial Neuron Devices for Probabilistic Computing

A spiking neuron implemented in VLSI

Electrical Stability of p-Channel Feedback Field-Effect Transistors Under Bias Stresses

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