Physics inspired compact modelling of [Formula: see text] based memristors

Area of Science:

• Nanoelectronics and Materials Science
• Hardware Security and Cryptography

Background:

• Memristors, particularly BiFeO3 (BFO)-based devices, are gaining interest for Internet of Things (IoT) hardware security due to properties like long retention, self-rectification, stochasticity, and fast switching.
• These BFO memristors are being explored for applications including physical unclonable function (PUF) key storage, artificial synapses, nonvolatile resistive switches, and reconfigurable logic.

Purpose of the Study:

• To present a physics-inspired 1D compact model for BFO memristors.
• To enable understanding and circuit simulations for BFO memristor applications, with a primary focus on PUFs.
• To model the resistive switching mechanism driven by electric field-induced vacancy migration and the intrinsic stochastic behavior of BFO memristors.

Main Methods:

• Development of a physics-inspired 1D compact model for BFO memristors.
• Utilized the cloud-in-a-cell scheme to model electric field-driven vacancy migration and stochastic behavior.
• Performed circuit simulations to validate the model's applicability.

Main Results:

• The developed model successfully reproduces the experimental current-voltage characteristics of BFO memristors.
• Analysis of the BFO memristor's response to electrical properties, temperature variations, and stress shows consistency with experimental findings.
• The model provides a robust framework for simulating BFO memristor behavior in various conditions.

Conclusions:

• The physics-inspired 1D compact model effectively captures the essential characteristics of BFO memristors.
• This validated model is suitable for simulating BFO memristor behavior and exploring their potential in hardware security applications like PUFs.
• The study confirms the viability of BFO memristors for advanced electronic applications requiring reliable and secure hardware primitives.