Analog-Digital Hybridity of Resistive Switching in Ion-Irradiated BiFeO3 Memristor for Synergistic Neuromorphic Functionality and Artificial Learning
Artikel i vetenskaplig tidskrift, 2024

Memristors-based neuromorphic devices represent emerging computing architectures to perform complex tasks by outpacing the traditional Von-Neumann architectures in terms of speed, and energy efficiency. In this work, the resistive switching (RS) behavior of sol-gel grown and ion-irradiated BFO films is investigated under electrical stimulus. The Ag/BFO/FTO memristors emulate a combination of digital and analog RS behavior within a single device. The possible mechanism of analog digital hybridity is addressed by considering the formation of the conducting filament by oxygen vacancies, Ag+ ions and Schottky barrier height modulation. The ion-irradiated BFO samples are analyzed using the Raman, XRD, and XPS studies. To uphold bioinspired synaptic actions, crucial synaptic functionalities like pair-pulse facilitation and long-term potentiation/depression are effectively achieved. More intricate synaptic behaviors are also demonstrated such as spike-time-dependent plasticity and Pavlovian classical conditioning, which represent the prominent attributes of both learning and forgetting behavior. Additionally, high pattern recognition accuracy (96.1%) is achieved in an artificial neural network simulation by using the synaptic weights of the memristors. This synergistic effect of digital and analog RS in ion-irradiated BFO can be beneficial for the emulation of complex learning behavior as well as its incorporation into low-power neuromorphic computing.

BFO memristor

Pavlovian classical conditioning

ion irradiation

neuromorphic computing

artificial neural network

Författare

Suman Roy

Homi Bhabha National Institute (HBNI)

Institute of Physics Bhubaneswar

Mousam Charan Sahu

Homi Bhabha National Institute (HBNI)

Institute of Physics Bhubaneswar

Universidad Complutense de Madrid

Anjan Kumar Jena

Institute of Physics Bhubaneswar

Maharaja Purna Chandra Autonomous Coll

Homi Bhabha National Institute (HBNI)

Sameer Kumar Mallik

Chalmers, Mikroteknologi och nanovetenskap, Kvantkomponentfysik

Roshan Padhan

Homi Bhabha National Institute (HBNI)

Institute of Physics Bhubaneswar

Jyoti Ranjan Mohanty

Indian Inst Technol Hyderabad

Satyaprakash Sahoo

Institute of Physics Bhubaneswar

Homi Bhabha National Institute (HBNI)

ADVANCED MATERIALS TECHNOLOGIES

2365-709X (ISSN)

Vol. In Press

Ämneskategorier

Bioinformatik (beräkningsbiologi)

DOI

10.1002/admt.202400557

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Senast uppdaterat

2024-11-08