Toward Plasmonic Neuronal Architectures at the Nanometer Scale

ABSTRACT We introduce a plasmonic computing platform that unites multiplexed optical inputs, static weighting, and nonlinear activation within a single nanoscale architecture—marking a critical step toward the realization of a plasmonic neural cell. In this approach, the orbital angular momentum (OAM) of light serves as an orthogonal and inherently parallel encoding scheme, enabling distinct optical channels to be directed into individual plasmonic waveguides. The weighting of input signals is governed by precisely engineered nanoscale gaps, whereas the system's nonlinear activation is revealed through two‐photon photoemission, spatially resolved using photoemission electron microscopy (PEEM). By confining electromagnetic fields far below the diffraction limit and minimizing surface plasmon polariton (SPP) propagation lengths, the design mitigates optical absorption losses while fully exploiting the unique field enhancement capabilities of plasmonic nanostructures. This physically integrated platform is compatible with scalable nanofabrication defined through a two‐step electron‐beam lithography (EBL) process and provides a compact, energy‐efficient building block for ultrafast neuromorphic photonic circuits. Together, these results outline a tangible route toward dense plasmonic neural networks capable of performing all‐optical information processing at the native speed of light.

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Publication Details

Journal
Nanophotonics
Published
2026-04-01
DOI
https://doi.org/10.1002/nap2.70066
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

Toward Plasmonic Neuronal Architectures at the Nanometer Scale

Tobias Eul, Martin Aeschlimann, Benjamin Stadtmüller, B. Lägel et al.
Nanophotonics
Neural Networks and Reservoir Computing
article

Toward Plasmonic Neuronal Architectures at the Nanometer Scale

Tobias Eul, Martin Aeschlimann, Benjamin Stadtmüller, B. Lägel, Mario F. Pfeiffer, Christopher G. O. Weiß, Emily Kruel
article en

Abstract

ABSTRACT We introduce a plasmonic computing platform that unites multiplexed optical inputs, static weighting, and nonlinear activation within a single nanoscale architecture—marking a critical step toward the realization of a plasmonic neural cell. In this approach, the orbital angular momentum (OAM) of light serves as an orthogonal and inherently parallel encoding scheme, enabling distinct optical channels to be directed into individual plasmonic waveguides. The weighting of input signals is governed by precisely engineered nanoscale gaps, whereas the system's nonlinear activation is revealed through two‐photon photoemission, spatially resolved using photoemission electron microscopy (PEEM). By confining electromagnetic fields far below the diffraction limit and minimizing surface plasmon polariton (SPP) propagation lengths, the design mitigates optical absorption losses while fully exploiting the unique field enhancement capabilities of plasmonic nanostructures. This physically integrated platform is compatible with scalable nanofabrication defined through a two‐step electron‐beam lithography (EBL) process and provides a compact, energy‐efficient building block for ultrafast neuromorphic photonic circuits. Together, these results outline a tangible route toward dense plasmonic neural networks capable of performing all‐optical information processing at the native speed of light.

NanophotonicsVol. 15(7)
University of Kaiserslautern (DE), University of Augsburg (DE), University of Applied Sciences Kaiserslautern (DE)
Affordable and clean energy
Openalex Percentile: Top 17%
Neural Networks and Reservoir Computing
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Toward Plasmonic Neuronal Architectures at the Nanometer Scale — Tobias Eul, Martin Aeschlimann, et al. · Nanophotonics (2026) | TGRS Research Map | TGRS