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Identifying and understanding the nonlinear behavior of memristive devices
Sahitya Yarragolla1, Torben Hemke2, Fares Jalled2
1Chair of Applied Electrodynamics and Plasma Technology, Ruhr University Bochum, Universitätsstraße 150, 44780, Bochum, Germany. sahitya@aept.ruhr-uni-bochum.de.
Scientific Reports
|December 31, 2024
Summary
Memristive devices exhibit nonlinear behavior crucial for neuromorphic computing. This study models these effects, using frequency spectra as unique fingerprints for memristive devices.
Area of Science:
- Materials Science
- Electrical Engineering
- Device Physics
Background:
- Nonlinearity is essential for hardware security and neuromorphic computing.
- Memristive devices display nonlinear current-voltage characteristics due to resistive, capacitive, and inertia effects.
- Understanding these effects is key to memristor device modeling.
Purpose of the Study:
- To develop a physics-inspired compact model for interface-type resistive random-access memory (RRAM) devices.
- To accurately simulate and capture the nonlinear current-voltage characteristics, including non-zero crossing hysteresis.
- To investigate the influence of frequency on memristive device behavior and propose frequency spectra as device fingerprints.
Main Methods:
- Employed a physics-inspired compact model to simulate RRAM devices (e.g., Au/BiFeO[Formula: see text]/Pt/Ti, Au/Nb[Formula: see text]O[Formula: see text]/Al[Formula: see text]O[Formula: see text]/Nb).
- Accounted for capacitive and inertia effects in the device model.
- Analyzed device response to varying frequencies and utilized Fourier series analysis for sinusoidal input voltages.
Main Results:
- Simulated current-voltage characteristics closely matched experimental data.
- Successfully captured non-zero crossing hysteresis attributed to capacitive and inductive effects.
- Observed a shift in nonlinear behavior with increasing frequency, characterized by reduced hysteresis.
- Identified influential harmonics and frequency components affecting RRAM operation.
Conclusions:
- The developed compact model accurately represents memristive device behavior, including complex nonlinearities.
- Frequency-dependent analysis reveals insights into memristor dynamics.
- Frequency spectra serve as effective fingerprints for identifying and characterizing memristive devices.
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