Highly reliable perovskite-based memristors using Ag nanoparticles/FA2PbI4 junctions for enhanced memory and optoelectronic synaptic performance

The hybrid nature of metal halide perovskites endows them with unique optoelectronic properties, which can be leveraged for developing next-generation memory devices with a small energy footprint. However, various challenges related to the stability and performance of these devices impede their practical implementation. To deal with this issue, in this work, an interface optimization technique was applied using a layer of Ag nanoparticles (NPs) that were soft landed onto the active perovskite layer without causing any significant damage. In contrast with the memristive devices having sputtered bulk silver metal contacts, the forming-free ITO/Ag NPs/FA2PbI4/Pt NPs/ITO memristor exhibited robust endurance properties (1011 cycles) accompanied by a big memory window (104) and a relatively low VSET (∼1.8 V). A numerical model was also applied to examine the origins of the switching effect. The transparent nature of both the electrode materials and the film of metal NPs permitted the emulation of synaptic properties using exclusively light sources. The frequency of the optical pulses was used to tune the conductance change, and a low power consumption per synaptic weight of ∼10 pJ was extracted during red light irradiation at 740 nm. Multicolor perception capabilities were also demonstrated using two other light sources in the visible range (450 and 550 nm). Our work provides a simple and effective approach for developing low power memory elements with enhanced performance for future optoelectronic neuromorphic applications.

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Publication Details

Journal
Applied Physics Letters
Published
2025-11-03
DOI
https://doi.org/10.1063/5.0299684
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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article

Highly reliable perovskite-based memristors using Ag nanoparticles/FA2PbI4 junctions for enhanced memory and optoelectronic synaptic performance

Θωμάς Στεργιόπουλος, C. Tsioustas, Panagiotis Bousoulas, Dimitris Tsoukalas et al.
1 citations
Applied Physics Letters
Advanced Memory and Neural Computing
0.73
article

Highly reliable perovskite-based memristors using Ag nanoparticles/FA2PbI4 junctions for enhanced memory and optoelectronic synaptic performance

Θωμάς Στεργιόπουλος, C. Tsioustas, Panagiotis Bousoulas, Dimitris Tsoukalas, Spyros Orfanoudakis, D. Spathi, V. Pagonis
article en
1 citations

Abstract

The hybrid nature of metal halide perovskites endows them with unique optoelectronic properties, which can be leveraged for developing next-generation memory devices with a small energy footprint. However, various challenges related to the stability and performance of these devices impede their practical implementation. To deal with this issue, in this work, an interface optimization technique was applied using a layer of Ag nanoparticles (NPs) that were soft landed onto the active perovskite layer without causing any significant damage. In contrast with the memristive devices having sputtered bulk silver metal contacts, the forming-free ITO/Ag NPs/FA2PbI4/Pt NPs/ITO memristor exhibited robust endurance properties (1011 cycles) accompanied by a big memory window (104) and a relatively low VSET (∼1.8 V). A numerical model was also applied to examine the origins of the switching effect. The transparent nature of both the electrode materials and the film of metal NPs permitted the emulation of synaptic properties using exclusively light sources. The frequency of the optical pulses was used to tune the conductance change, and a low power consumption per synaptic weight of ∼10 pJ was extracted during red light irradiation at 740 nm. Multicolor perception capabilities were also demonstrated using two other light sources in the visible range (450 and 550 nm). Our work provides a simple and effective approach for developing low power memory elements with enhanced performance for future optoelectronic neuromorphic applications.

Applied Physics LettersVol. 127(18)
National Technical University of Athens (GR), National Centre of Scientific Research "Demokritos" (GR)
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
0.73
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