Printed High‐Entropy Prussian Blue Analogs for Advanced Non‐Volatile Memristive Devices

Non-volatile memristors dynamically switch between high (HRS) and low resistance states (LRS) in response to electrical stimuli, essential for electronic memories, neuromorphic computing, and artificial intelligence. High-entropy Prussian blue analogs (HE-PBAs) are promising insertion-type battery materials due to their diverse composition, high structural integrity, and favorable ionic conductivity. This work proposes a non-volatile, bipolar memristor based on HE-PBA. The device, featuring an active layer of HE-PBA sandwiched between Ag and ITO electrodes, is fabricated by inkjet printing and microplotting. The conduction mechanism of the Ag/HE-PBA/ITO device is systematically investigated. The results indicate that the transition between HRS and LRS is driven by an insulating-metallic transition, triggered by extraction/insertion of highly mobile Na<sup>+</sup> ions upon application of an electric field. The memristor operates through a low-energy process akin to Na<sup>+</sup> shuttling in Na-ion batteries rather than depending on formation/rupture of Ag filaments. Notably, it showcases promising characteristics, including non-volatility, self-compliance, and forming-free behavior, and further exhibits low operation voltage (V<sub>SET</sub> = -0.26 V, V<sub>RESET</sub> = 0.36 V), low power consumption (P<sub>SET</sub> = 26 µW, P<sub>RESET</sub> = 8.0 µW), and a high R<sub>OFF</sub>/R<sub>ON</sub> ratio of 10<sup>4</sup>. This underscores the potential of high-entropy insertion materials for developing printed memristors with distinct operation mechanisms.

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Publication Details

Journal
Advanced Materials
Published
2024-11-20
DOI
https://doi.org/10.1002/adma.202410060
Citations
8
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.67

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Printed High‐Entropy Prussian Blue Analogs for Advanced Non‐Volatile Memristive Devices

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Advanced Materials
Advanced Memory and Neural Computing
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article

Printed High‐Entropy Prussian Blue Analogs for Advanced Non‐Volatile Memristive Devices

Simon Schweidler, Jasmin Aghassi‐Hagmann, Yanjiao Ma, Torsten Brezesinski, Gabriel Cadilha Marques, Yin‐Ying Ting, Hongrong Hu, Yuting Dai, Piotr M. Kowalski, Horst Hahn, Thomas Diemant, Ben Breitung, Jing Lin, Yueyue He
article en
8 citations

Abstract

Non-volatile memristors dynamically switch between high (HRS) and low resistance states (LRS) in response to electrical stimuli, essential for electronic memories, neuromorphic computing, and artificial intelligence. High-entropy Prussian blue analogs (HE-PBAs) are promising insertion-type battery materials due to their diverse composition, high structural integrity, and favorable ionic conductivity. This work proposes a non-volatile, bipolar memristor based on HE-PBA. The device, featuring an active layer of HE-PBA sandwiched between Ag and ITO electrodes, is fabricated by inkjet printing and microplotting. The conduction mechanism of the Ag/HE-PBA/ITO device is systematically investigated. The results indicate that the transition between HRS and LRS is driven by an insulating-metallic transition, triggered by extraction/insertion of highly mobile Na<sup>+</sup> ions upon application of an electric field. The memristor operates through a low-energy process akin to Na<sup>+</sup> shuttling in Na-ion batteries rather than depending on formation/rupture of Ag filaments. Notably, it showcases promising characteristics, including non-volatility, self-compliance, and forming-free behavior, and further exhibits low operation voltage (V<sub>SET</sub> = -0.26 V, V<sub>RESET</sub> = 0.36 V), low power consumption (P<sub>SET</sub> = 26 µW, P<sub>RESET</sub> = 8.0 µW), and a high R<sub>OFF</sub>/R<sub>ON</sub> ratio of 10<sup>4</sup>. This underscores the potential of high-entropy insertion materials for developing printed memristors with distinct operation mechanisms.

Advanced MaterialsVol. 37(8)
Karlsruhe Institute of Technology (DE), Nanjing Normal University (CN), Forschungszentrum Jülich (DE), Technische Universität Darmstadt (DE), Helmholtz-Institute Ulm (DE), Jülich Aachen Research Alliance (DE), University of Oklahoma (US), RWTH Aachen University (DE)
Deutsche Forschungsgemeinschaft
Affordable and clean energy
Openalex Percentile: Top 15%
Advanced Memory and Neural Computing
1.67
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