Real‐Time Correlation Detection via Online Learning of a Spiking Neural Network with a Conductive‐Bridge Neuron

Abstract

Neumann architectures to overcome the von Neumann bottleneck in artificial intelligence applications. [1][2][3][4][5][6][7][8] Neuromorphic architectures, especially spiking neural networks (SNNs), consume considerably less power (≈20 mW), than conventional von Neumann computing architectures (≈100 W). [9] As the main building block of SNNs, spiking neurons, especially complementary-metal-oxidesemiconductor field-effect transistor (C-MOSFET)-based neurons, have been intensively researched. However, the density of such neurons is limited because of the extremely large area of the capacitor (>500 μm 2 per capacitor) required to emulate the integrate function and achieve sufficient capacitance (i.e., several pF per capacitor). [10][11][12][13][14][15][16][17][18] To overcome this problem, the use of capacitor-less spiking neurons has been recommended. Recently, several researchers reported spiking neurons that can exhibit the integrate functionality, based on frameworks such as the phase-change memory, [19,20] resistive-random-access memory, [21][22][23][24][25] conductive-bridge-random-access memory, [26,27] and partially depleted silicon-on-insulator. [28] A spiking neuron principally needs a spike neuron device to emulate the integrate function and a sensing amplifier circuit to generate the fire function in a scheme known as integrate-and-fire. However, the existing studies only empirically presented spiking neuron devices to emulate the integrate function. Certain researchers designed a neuron in software by using a sense amplifier circuit, a controller for operating the neuron, a SNN containing neurons, and synapses, and a pattern recognition test was conducted using software simulations. However, only the realization of a spiking neuron was demonstrated as all the SNN operations based on spiking neuron devices emulating the integrate function were conducted via simulations.This study represents the first attempt at developing a conductive-bridge neuron emulating an integrate-and-fire function as an alternative to conventional C-MOSFET-based spiking neurons. The neuron was composed of a conductive-bridge-neuron device, sensing amplifier, and latch circuit. The conductivebridge-neuron device was fabricated in a simple manner by adopting a vertical device structure including a CuTe top electrode, TiO 2 resistive layer, and TiN bottom electrode. The Cu atoms diffused from the CuTe top electrode could easily form Cu filaments in the TiO 2 resistive layer. This phenomenon is of significance because Cu filaments in the TiO 2 resistiveThe neuronal density of complementary metal-oxide-semiconductor field-effect transistor-based neurons is limited because of the use of capacitors. Therefore, a novel neuron is fabricated using a conductive-bridge-neuron device, currentmirror-type sense amplifier, latch, micro-controller-unit, and digital-analogconverters. This neuron exhibits a typical integrate-and-fire function; in particular, the generation frequency of the fire spikes at the neuron exponentially increases with the i...

Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era

“…Due to the high solubility of Ag, the thin filament readily “dissolves” when the excitation voltage is reduced. Exploiting this characteristic, several works 142,144,145 made use of this behaviour to implement the integrate-fire function of a neuron. The circuit comprises a capacitor connected parallel to the threshold switch.…”

Single silicon synaptic device for stochastic binary spike-timing-dependent plasticity

“…Spiking neural networks (SNNs) have become powerful neuromorphic computing paradigms because they feature massive parallelism and low energy consumption [1][2][3]. Previously, emerging memory devices, such as resistive memories [4][5][6], phase change memories [7][8][9], ferroelectric memories [10][11][12], spintronic memories [13][14][15], and conductive bridge memories [16][17][18], were reported to realize the hardware implementation of SNNs. However, they are limited by device variability in the fabrication processes and nanoscale physical effects.…”