Amorphous, Highly Conductive Pr 0.7 Ca 0.3 MnO 3 for Area‐Dependent Resistive Switching AlO x Bilayer Devices

ABSTRACT Memristive Pr 0.7 Ca 0.3 MnO 3 (PCMO) heterostructures exhibit area‐dependent resistive switching via a valence change mechanism, making them promising for neuromorphic architectures. A major challenge in PCMO‐based memory is higher‐dimensional lattice defects that affect oxygen‐vacancy migration and concentration. This study mitigates these defects using highly conductive amorphous PCMO fabricated via a CMOS back‐end‐of‐line‐compatible process and compares it with low‐conductive amorphous and polycrystalline PCMO. The resistance differences are attributed to changes in electronic mobility, based on the analysis of short‐ and long‐range order, Mn–O hybridization, and Mn valence state. AlO x /qa‐PCMO devices showed the highest ON/OFF ratio compared to low‐conductive amorphous and polycrystalline PCMO, because the field‐accelerated oxygen vacancy movement switches the mechanism from Poole–Frenkel emission in the LRS to trap‐assisted tunneling in the HRS. The mechanism change was identified by systematically analyzing the I–V asymmetry, device band diagrams for different PCMO types, and shape changes in the I–V curve fits. The band diagrams were calculated from the measured bandgaps and work functions of the different PCMO types. Analysis of the electric field distribution in the devices showed a clear correlation between the pre‐switching field strength in AlO x and the resulting ON/OFF ratio.

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Publication Details

Journal
Advanced Electronic Materials
Published
2026-09-04
DOI
https://doi.org/10.1002/aelm.202500556
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Amorphous, Highly Conductive Pr 0.7 Ca 0.3 MnO 3 for Area‐Dependent Resistive Switching AlO x Bilayer Devices

M. Buczek, Iliyas T. Dossayev, Yen‐Po Liu, Susanne Hoffmann‐Eifert et al.
Advanced Electronic Materials
Advanced Memory and Neural Computing
article

Amorphous, Highly Conductive Pr 0.7 Ca 0.3 MnO 3 for Area‐Dependent Resistive Switching AlO x Bilayer Devices

M. Buczek, Iliyas T. Dossayev, Yen‐Po Liu, Susanne Hoffmann‐Eifert, Regina Dittmann, David N. Mueller, Karsten Bittkau, Zoe Moos, Clemens Wittberg, Kalle Goß, Mohammad Hassan Sultani, Stephan Menzel, Zhenhao Liu
article en

Abstract

ABSTRACT Memristive Pr 0.7 Ca 0.3 MnO 3 (PCMO) heterostructures exhibit area‐dependent resistive switching via a valence change mechanism, making them promising for neuromorphic architectures. A major challenge in PCMO‐based memory is higher‐dimensional lattice defects that affect oxygen‐vacancy migration and concentration. This study mitigates these defects using highly conductive amorphous PCMO fabricated via a CMOS back‐end‐of‐line‐compatible process and compares it with low‐conductive amorphous and polycrystalline PCMO. The resistance differences are attributed to changes in electronic mobility, based on the analysis of short‐ and long‐range order, Mn–O hybridization, and Mn valence state. AlO x /qa‐PCMO devices showed the highest ON/OFF ratio compared to low‐conductive amorphous and polycrystalline PCMO, because the field‐accelerated oxygen vacancy movement switches the mechanism from Poole–Frenkel emission in the LRS to trap‐assisted tunneling in the HRS. The mechanism change was identified by systematically analyzing the I–V asymmetry, device band diagrams for different PCMO types, and shape changes in the I–V curve fits. The band diagrams were calculated from the measured bandgaps and work functions of the different PCMO types. Analysis of the electric field distribution in the devices showed a clear correlation between the pre‐switching field strength in AlO x and the resulting ON/OFF ratio.

Advanced Electronic Materials
Ernst Ruska Centre (DE), Advanced Materials and Devices (United States) (US)
Deutsche Forschungsgemeinschaft, VINNOVA, Svenska Forskningsrådet Formas, Vetenskapsrådet, Bundesministerium für Forschung und Technologie, Deutsches Elektronen-Synchrotron
Openalex Percentile: Top 20%
Advanced Memory and Neural Computing
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