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.
Authors
- Θωμάς Στεργιόπουλος (ORCID: https://orcid.org/0000-0002-6701-9975)
- I. Zergioti (ORCID: https://orcid.org/0000-0001-6341-4495)
- Athanassios G. Kontos (ORCID: https://orcid.org/0000-0001-9193-0198)
- Nikolaos Chalmpes (ORCID: https://orcid.org/0000-0003-2744-5934)
- Ilias Cheliotis (ORCID: https://orcid.org/0009-0005-9770-5000)
- Myrto Nikolopoulou
- Panagiotis Oikonomopoulos (ORCID: https://orcid.org/0000-0002-6417-8241)
- D. Tsoukalas (ORCID: https://orcid.org/0000-0001-5189-3396)
- Panagiotis Bousoulas (ORCID: https://orcid.org/0000-0002-5395-0777)
- Emmanuel P. Giannelis (ORCID: https://orcid.org/0000-0001-6158-3738)
- Polychronis Tsipas (ORCID: https://orcid.org/0000-0001-9064-9601)
- Spyros Orfanoudakis (ORCID: https://orcid.org/0009-0000-9763-5560)
- Leonidas Tsetseris (ORCID: https://orcid.org/0000-0002-0330-0813)
- Panagiotis Dallas (ORCID: https://orcid.org/0000-0002-9893-7867)
- Amaia Zurutuza (ORCID: https://orcid.org/0000-0001-6376-0224)
- Roupen Vartian (ORCID: https://orcid.org/0009-0009-0779-1697)
- Chalalampos Tsioustas
Institutions
- 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)
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
- Field-Weighted Citation Impact
- 0.00