Analog Integrate-And-Fire Neuron in 7 nm FinFET Technology

In this work an analog Integrate-And-Fire Neuron is described and implemented in 7 nm FinFET technology. The circuit operates at a power supply voltage of 750 mV and features an operational-amplifier-based comparator to set an explicit spiking threshold voltage. A capacitive positive feedback is employed, alongside a stage which resets the membrane potential after a spike generation. The circuit was simulated with transistor models which account for parasitics effects and transient simulations for nA input synaptic currents are reported, with a peak current drawn from the power supply of 4 uA. The circuit operates on an accelerated timescale from units to tens of us, is equipped with external voltage biases to tune the membrane leak current, refractory period, and pulse width after silicon fabrication, hence suitable for developing mixed-signal neuromorphic architectures.

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Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1109/icicdt65192.2025.11077981
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Analog Integrate-And-Fire Neuron in 7 nm FinFET Technology

A. Basçhirotto, Lorenzo Stevenazzi, Marcello De Matteis
Advanced Memory and Neural Computing
article

Analog Integrate-And-Fire Neuron in 7 nm FinFET Technology

A. Basçhirotto, Lorenzo Stevenazzi, Marcello De Matteis
article en

Abstract

In this work an analog Integrate-And-Fire Neuron is described and implemented in 7 nm FinFET technology. The circuit operates at a power supply voltage of 750 mV and features an operational-amplifier-based comparator to set an explicit spiking threshold voltage. A capacitive positive feedback is employed, alongside a stage which resets the membrane potential after a spike generation. The circuit was simulated with transistor models which account for parasitics effects and transient simulations for nA input synaptic currents are reported, with a peak current drawn from the power supply of 4 uA. The circuit operates on an accelerated timescale from units to tens of us, is equipped with external voltage biases to tune the membrane leak current, refractory period, and pulse width after silicon fabrication, hence suitable for developing mixed-signal neuromorphic architectures.

University of Milano-Bicocca (IT)
Affordable and clean energy
Openalex Percentile: Top 82%
Advanced Memory and Neural Computing
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