Nonlinear behavior of memristive devices for hardware security primitives and neuromorphic computing systems

TL;DR

This paper investigates the nonlinear current-voltage behavior of memristive devices, modeling physical effects, validating with experiments, and exploring frequency-dependent dynamics for security and neuromorphic applications.

Contribution

It introduces a physics-inspired compact model capturing resistive, capacitive, and inertia effects in memristive devices, and proposes frequency spectra as device fingerprints.

Findings

Simulated I-V characteristics match experimental data.

Increasing frequency reduces hysteresis and enhances chaotic behavior.

Frequency spectra can serve as unique device fingerprints.

Abstract

Nonlinearity is a crucial characteristic for implementing hardware security primitives or neuromorphic computing systems. The main feature of all memristive devices is this nonlinear behavior observed in their current-voltage characteristics. To comprehend the nonlinear behavior, we have to understand the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in these devices. These effects originate from corresponding physical and chemical processes in memristive devices. A physics-inspired compact model is employed to model and simulate interface-type RRAMs such as Au/BiFeO$_{3}$/Pt/Ti, Au/Nb$_{\rm x}$O$_{\rm y}$/Al$_{2}$O$_{3}$/Nb, while accounting for the modeling of capacitive and inertia effects. The simulated current-voltage characteristics align well with experimental data and accurately capture the non-zero crossing hysteresis generated by capacitive and inductive effects. This study examines the response of two devices to increasing frequencies, revealing a shift in their nonlinear behavior characterized by a reduced hysteresis range and increased chaotic behavior, as observed through internal state attractors. Fourier series analysis utilizing a sinusoidal input voltage of varying amplitudes and frequencies indicates harmonics or frequency components that considerably influence the functioning of RRAMs. Moreover, we propose and demonstrate the use of the frequency spectra as one of the fingerprints for memristive devices.