Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide This study explores the area-dependent resistive switching (RS) characteristics of Gd 0.2 Ca 0.8 MnO 3 (GCMO)-based memristors with aluminum (Al) and gold (Au) electrodes, emphasizing their potential for neuromorphic computing applications. Using a combination of electrical measurements and X-ray photoelectron spectroscopy (XPS), we demonstrate that the high-resistance (HRS) and low-resistance (LRS) states exhibit predictable scaling with device area, with HRS resistances ranging from 10 7 to 10 8 Ω and LRS from 10 5 to 10 7 Ω, supporting the hypothesis of interface-type RS. XPS depth profiling revealed notable differences in AlO x interfacial layer composition between HRS and LRS, with a higher oxide content and a widened interfacial region in HRS. Additionally, the multistate RS capability of up to ten distinct levels was achieved by modulating applied voltages, highlighting GCMO’s suitability as a material for synaptic weight storage in artificial neural networks. Our findings underscore GCMO’s promise for energy-efficient, scalable memristor-based systems.
Source: ACS Applied Electronic Materials
Authors: Anni Antola, Johanna Laaksonen, Hannu Huhtinen, Ilari Angervo, Sari Granroth, Alejandro Schulman, Pekka Laukkanen, Petriina Paturi
Institutions: University of Turku, Universidad de Salamanca
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W4409704395