Benchmarking Stochasticity behind Reproducibility: Denoising Strategies in Ta2O5 Memristors

TL;DR

This paper benchmarks noise in Ta2O5 memristors and proposes denoising strategies to improve reproducibility in artificial synapses.

Contribution

A novel noise benchmarking and reduction protocol for memristors, including voltage-dependent noise tracking and subthreshold denoising.

Findings

Voltage-dependent noise characteristics were tracked along resistive switching I(V) curves.

Voltage cycling schemes can tune noise levels in filamentary Ta2O5 memristors.

Subthreshold voltage cycles enable denoising without resistive switching.

Abstract

Reproducibility, endurance, driftless data retention,
and fine
resolution of the programmable conductance weights are key technological
requirements against memristive artificial synapses in neural network
applications. However, the inherent fluctuations in the active volume
impose severe constraints on the weight resolution. In order to understand
and push these limits, a comprehensive noise benchmarking and noise
reduction protocol is introduced. Our approach goes beyond the measurement
of steady-state readout noise levels and tracks the voltage-dependent
noise characteristics all along the resistive switching I(V) curves. Furthermore, we investigate the tunability
of the noise level by dedicated voltage cycling schemes in our filamentary
Ta2O5 memristors. This analysis highlights a
broad order-of-magnitude variability of the possible noise levels
behind seemingly reproducible switching cycles. Our nonlinear noise
spectroscopy measurements identify a subthreshold voltage region with
voltage-boosted fluctuations. This voltage range enables the reconfiguration
of the fluctuators without resistive switching, yielding a highly
denoised state within a few subthreshold cycles.

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