Flexible 2D Perovskite–hBN Optoelectronic Dendritic Neurons for Energy-Efficient Information Processing

Artificial dendrites, capable of nonlinear signal integration, are key building blocks for next-generation neuromorphic hardware, yet their realization in soft, solution-processed materials remains hindered by instability and poorly controlled ion dynamics. Here, we report an artificial dendrite element based on a vertically orchestrated ITO/Ag nanoparticle (NP)/hBN/(PEA)2MA4Pb5I16/Pt NPS/ITO heterostructure, where a monolayer hBN barrier governs metal-ion injection with atomic precision. The hBN-mediated field redistribution enables graded and time-dependent filament initiation within the oriented two-dimensional (2D) Ruddlesden-Popper perovskite, producing highly nonlinear current integration analogous to excitatory postsynaptic potentials. Under consecutively correlated stimuli, the device exhibits hallmark dendritic behaviors-temporal summation, threshold-dependent firing, refractory period, and wavelength-sensitive integration-arising from controlled Ag+ accumulation and reversible filament branching. The devices operate at ∼1 V, demonstrate stable and reproducible transient responses over >1011 cycles, and retain robust performance under mechanical deformation. Complementary physics-based numerical modeling and Density Functional Theory (DFT) calculations elucidate the influence of hBN on Ag+ diffusivity, as well as on filament formation. The dendritic neurons can efficiently encode velocity and direction, enabling compact feature maps for accurate motion classification and trajectory tracking. Our results introduce a promising material platform for high-density, energy-efficient neuromorphic processors capable of bio-inspired spatiotemporal learning.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-10-16
DOI
https://doi.org/10.1021/acsami.6c13400
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Flexible 2D Perovskite–hBN Optoelectronic Dendritic Neurons for Energy-Efficient Information Processing

Θωμάς Στεργιόπουλος, I. Zergioti, Athanassios G. Kontos, Nikolaos Chalmpes et al.
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

Flexible 2D Perovskite–hBN Optoelectronic Dendritic Neurons for Energy-Efficient Information Processing

Θωμάς Στεργιόπουλος, I. Zergioti, Athanassios G. Kontos, Nikolaos Chalmpes, Ilias Cheliotis, Myrto Nikolopoulou, Panagiotis Oikonomopoulos, D. Tsoukalas, Panagiotis Bousoulas, Emmanuel P. Giannelis, Polychronis Tsipas, Spyros Orfanoudakis, Leonidas Tsetseris, Panagiotis Dallas, Amaia Zurutuza, Roupen Vartian, Chalalampos Tsioustas
article en

Abstract

Artificial dendrites, capable of nonlinear signal integration, are key building blocks for next-generation neuromorphic hardware, yet their realization in soft, solution-processed materials remains hindered by instability and poorly controlled ion dynamics. Here, we report an artificial dendrite element based on a vertically orchestrated ITO/Ag nanoparticle (NP)/hBN/(PEA)2MA4Pb5I16/Pt NPS/ITO heterostructure, where a monolayer hBN barrier governs metal-ion injection with atomic precision. The hBN-mediated field redistribution enables graded and time-dependent filament initiation within the oriented two-dimensional (2D) Ruddlesden-Popper perovskite, producing highly nonlinear current integration analogous to excitatory postsynaptic potentials. Under consecutively correlated stimuli, the device exhibits hallmark dendritic behaviors-temporal summation, threshold-dependent firing, refractory period, and wavelength-sensitive integration-arising from controlled Ag+ accumulation and reversible filament branching. The devices operate at ∼1 V, demonstrate stable and reproducible transient responses over >1011 cycles, and retain robust performance under mechanical deformation. Complementary physics-based numerical modeling and Density Functional Theory (DFT) calculations elucidate the influence of hBN on Ag+ diffusivity, as well as on filament formation. The dendritic neurons can efficiently encode velocity and direction, enabling compact feature maps for accurate motion classification and trajectory tracking. Our results introduce a promising material platform for high-density, energy-efficient neuromorphic processors capable of bio-inspired spatiotemporal learning.

ACS Applied Materials & Interfaces
National Technical University of Athens (GR), National and Kapodistrian University of Athens (GR), National Centre of Scientific Research "Demokritos" (GR), Cornell University (US), National Hellenic Research Foundation (GR), Graphenea (Spain) (ES)
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
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