Quantum Resistance in Multilayer Graphene-BiFeO3 Memristor for Brain-Inspired Computing

TL;DR

This paper demonstrates quantum conductance states in BiFeO3-graphene memristors, enabling high-density data storage and neuromorphic computing through controllable quantum states and synaptic emulation.

Contribution

It introduces a novel multilayer-graphene integrated BiFeO3 memristor exhibiting bidirectional quantum conductance states with higher-order tunability for quantum memory and neuromorphic applications.

Findings

Quantum conductance states are achieved during SET and RESET processes.

Devices can emulate synaptic potentiation and depression.

Quantized conductance enables high-accuracy neural network recognition.

Abstract

In the era of big data and the Internet of Things, quantum-level control of conductance states offers a promising route toward high-density data storage and brain-inspired neuromorphic computing. Although quantum conductance (QC) phenomena have been demonstrated in various metal oxide memristors, achieving reliable and precise control over quantized states remains in its infancy. Here, we demonstrate bidirectional quantum conductance states in multifunctional BiFeO3 (BFO) perovskite memristors integrated with multilayer-graphene contacts, enabling higher-order tunability and revealing the potential of perovskite-2D heterostructures for quantum-engineered memory and computing devices. XPS analysis provides detailed insights into oxygen vacancy dynamics in BFO, whereas first-principles density functional theory calculations clearly reveal a strong localized electric field at the graphene-BFO interface. Our devices exhibit current-controlled higher-order QC transitions facilitated by quantum point contact formation, giving rise to quantized conductance states during both SET and RESET processes. Time-lag correlation maps quantify the stochastic evolution of QC states under dynamic voltage-pulse tuning schemes. Notably, the quantized conductance states effectively emulate synaptic potentiation and depression, enabling precise weight modulation for high-accuracy image and digit recognition in convolutional neural networks. These findings establish perovskite-2D heterostructures as promising candidates for QC-driven resistive switching and demonstrate their potential for developing controllable quantum memristors.