A New Threshold Switching Device With Tunable Negative Differential Resistance Based on ErMnO <sub>3</sub> Polymorphs

ABSTRACT Negative differential resistance (NDR) devices have emerged as promising building blocks for neuromorphic computing due to their inherent nonlinear and threshold‐dependent electrical behavior. In particular, current‐controlled NDR devices can exhibit abrupt switching, self‐sustained oscillations, and volatility – key characteristics analogous to the spiking dynamics of biological neurons. The NDR is typically driven by thermal runaway effects, or by an insulator‐to‐metal transition, with materials such as VO 2 , NbO x , TaO x, and complex oxides like nikelates, cobaltites, or manganites. However, these systems often suffer from high forming and operating voltages, large Joule heating, limited device endurance, and no or little tunability. Here, we demonstrate a novel NDR device based on polycrystalline ErMnO 3 comprising conducting orthorhombic and insulating hexagonal phases. Pt/ErMnO 3 /Pt memory devices exhibit unipolar, symmetric, forming‐free threshold switching with a low threshold voltage of 2.1 V, a memory window of 1.1 V, and endurance over 2 × 10 4 cycles. Joule‐heating‐enhanced Poole–Frenkel conduction occurs in the orthorhombic phase, while the insulating hexagonal phase prevents excessive heating and breakdown. Tunability is achieved by controlling the polymorph ratio and the orthorhombic phase conductivity. ErMnO 3 polymorphs thus offer a versatile platform to engineer the electrical characteristics of NDR devices for low‐power, reliable neuromorphic applications.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-04-01
DOI
https://doi.org/10.1002/adfm.202527736
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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A New Threshold Switching Device With Tunable Negative Differential Resistance Based on ErMnO 3 Polymorphs

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A New Threshold Switching Device With Tunable Negative Differential Resistance Based on ErMnO 3 Polymorphs

Ronald Tetzlaff, Thanh Luan Phan, Wassim Hamouda, Veeresh Deshpande, Richard Schroedter, Florian Maudet, Ahmet Samil Demirkol, Catherine Dubourdieu
article en

Abstract

ABSTRACT Negative differential resistance (NDR) devices have emerged as promising building blocks for neuromorphic computing due to their inherent nonlinear and threshold‐dependent electrical behavior. In particular, current‐controlled NDR devices can exhibit abrupt switching, self‐sustained oscillations, and volatility – key characteristics analogous to the spiking dynamics of biological neurons. The NDR is typically driven by thermal runaway effects, or by an insulator‐to‐metal transition, with materials such as VO 2 , NbO x , TaO x, and complex oxides like nikelates, cobaltites, or manganites. However, these systems often suffer from high forming and operating voltages, large Joule heating, limited device endurance, and no or little tunability. Here, we demonstrate a novel NDR device based on polycrystalline ErMnO 3 comprising conducting orthorhombic and insulating hexagonal phases. Pt/ErMnO 3 /Pt memory devices exhibit unipolar, symmetric, forming‐free threshold switching with a low threshold voltage of 2.1 V, a memory window of 1.1 V, and endurance over 2 × 10 4 cycles. Joule‐heating‐enhanced Poole–Frenkel conduction occurs in the orthorhombic phase, while the insulating hexagonal phase prevents excessive heating and breakdown. Tunability is achieved by controlling the polymorph ratio and the orthorhombic phase conductivity. ErMnO 3 polymorphs thus offer a versatile platform to engineer the electrical characteristics of NDR devices for low‐power, reliable neuromorphic applications.

Advanced Functional Materials
Helmholtz-Zentrum Berlin für Materialien und Energie (DE), Hochschule für Technik und Wirtschaft Dresden – University of Applied Sciences (DE), Freie Universität Berlin (DE), Technische Universität Dresden (DE)
Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 33%
Advanced Memory and Neural Computing
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