Fast Electronic Memristors with Improved Retention and Synaptic Functionality Enabled by Tri-TaOx Engineering

Artificial synaptic memristors require fast switching, stable retention, low variability, and linear conductance modulation, but achieving these properties simultaneously is difficult in electronic bipolar resistive switching (e-BRS) devices. In this study, a tri-TaOx memristor is demonstrated by interposing a defective oxygen-deficient TaOx switching layer (Tx, x ≈ 1.8) between insulating near-stoichiometric TaOy interfacial layers (Ty, y ≈ 2.5) at the electrode interfaces. The optimized Ty/Tx/Ty device exhibits forming-free electronic switching, an on/off ratio of 138, a rectification ratio of ∼1.1 × 103, and low cycle-to-cycle and device-to-device variations. Stable retention is maintained in both resistance states, with an on/off ratio greater than 10 over 103 s at room temperature and 120 °C. Under alternating-current pulse operation, the device is programmed by 10 ns SET/RESET pulses and exhibits reliable conductance modulation. The memory window is maintained up to approximately 109 cycles, followed by gradual degradation and final failure near 2 × 109 cycles. The Ty/Tx/Ty tri-TaOx memristor also exhibits gradual potentiation/depression under an optimal identical-pulse condition of ±2.6 V and 400 ns, with low nonlinearity, low-conductance variation, and symmetric spike-timing-dependent plasticity behavior. These results demonstrate that the Ty/Tx/Ty tri-TaOx device outperforms previous e-BRS devices as a high-performance electronic synaptic memristor for both artificial deep neural network accelerators and low-power spiking-driven neuromorphic systems.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-09-18
DOI
https://doi.org/10.1021/acsami.6c14083
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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Fast Electronic Memristors with Improved Retention and Synaptic Functionality Enabled by Tri-TaOx Engineering

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Fast Electronic Memristors with Improved Retention and Synaptic Functionality Enabled by Tri-TaOx Engineering

Cheol Seong Hwang, Kyung Seok Woo, Han Yong Jeong, Jong Hoon Shin, Janguk Han, Hyungjun Park, Hyun Wook Kim
article en

Abstract

Artificial synaptic memristors require fast switching, stable retention, low variability, and linear conductance modulation, but achieving these properties simultaneously is difficult in electronic bipolar resistive switching (e-BRS) devices. In this study, a tri-TaOx memristor is demonstrated by interposing a defective oxygen-deficient TaOx switching layer (Tx, x ≈ 1.8) between insulating near-stoichiometric TaOy interfacial layers (Ty, y ≈ 2.5) at the electrode interfaces. The optimized Ty/Tx/Ty device exhibits forming-free electronic switching, an on/off ratio of 138, a rectification ratio of ∼1.1 × 103, and low cycle-to-cycle and device-to-device variations. Stable retention is maintained in both resistance states, with an on/off ratio greater than 10 over 103 s at room temperature and 120 °C. Under alternating-current pulse operation, the device is programmed by 10 ns SET/RESET pulses and exhibits reliable conductance modulation. The memory window is maintained up to approximately 109 cycles, followed by gradual degradation and final failure near 2 × 109 cycles. The Ty/Tx/Ty tri-TaOx memristor also exhibits gradual potentiation/depression under an optimal identical-pulse condition of ±2.6 V and 400 ns, with low nonlinearity, low-conductance variation, and symmetric spike-timing-dependent plasticity behavior. These results demonstrate that the Ty/Tx/Ty tri-TaOx device outperforms previous e-BRS devices as a high-performance electronic synaptic memristor for both artificial deep neural network accelerators and low-power spiking-driven neuromorphic systems.

ACS Applied Materials & Interfaces
Seoul National University (KR), Ulsan National Institute of Science and Technology (KR)
National Research Foundation of Korea
Affordable and clean energy
Openalex Percentile: Top 20%
Advanced Memory and Neural Computing
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