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Neural network connectivity by optical broadcasting between III-V nanowires

Draguns, Kristians ; Flodgren, Vidar LU ; Winge, David LU ; Serafini, Alfredo ; Atvars, Aigars ; Alnis, Janis and Mikkelsen, Anders LU (2025) In Nanophotonics 14(15). p.2575-2585
Abstract

Biological neural network functionality depends on the vast number of connections between nodes, which can be challenging to implement artificially. One radical solution is to replace physical wiring with broadcasting of signals between the artificial neurons. We explore an implementation of this concept by light emitting/receiving III-V semiconductor nanowire neurons in a quasi-2D waveguide. They broadcast light in anisotropic patterns and specific regions in the nanowires are sensitised to exciting and inhibiting light signals. Weights of connections between nodes can then be tailored using the geometric light absorption/emission patterns. Through detailed simulations, we determine the connection strength based on rotation and... (More)

Biological neural network functionality depends on the vast number of connections between nodes, which can be challenging to implement artificially. One radical solution is to replace physical wiring with broadcasting of signals between the artificial neurons. We explore an implementation of this concept by light emitting/receiving III-V semiconductor nanowire neurons in a quasi-2D waveguide. They broadcast light in anisotropic patterns and specific regions in the nanowires are sensitised to exciting and inhibiting light signals. Weights of connections between nodes can then be tailored using the geometric light absorption/emission patterns. Through detailed simulations, we determine the connection strength based on rotation and separation between the nanowires. Our findings reveal that complex weight distributions are possible, indicating that specific neuron geometric patterns can achieve highly variable connectivity as needed for neural networks. An important design parameter is matching the wavelength to the specific physical dimensions of the network. To demonstrate applicability, we simulate a reservoir neural network using a hexagonal pattern of nanowires with random angular orientations, displaying its ability to perform chaotic time series prediction. The design is compatible with integration on Si substrates and can be extended to other nanophotonic components.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
III-V, nanowires, optical neural networks, semiconductors
in
Nanophotonics
volume
14
issue
15
pages
11 pages
publisher
De Gruyter
external identifiers
  • pmid:40771416
  • scopus:105009933734
ISSN
2192-8614
DOI
10.1515/nanoph-2025-0035
language
English
LU publication?
yes
id
efa619c0-445e-47df-9884-7c178e8b90ca
date added to LUP
2025-11-05 13:04:02
date last changed
2025-11-06 03:00:10
@article{efa619c0-445e-47df-9884-7c178e8b90ca,
  abstract     = {{<p>Biological neural network functionality depends on the vast number of connections between nodes, which can be challenging to implement artificially. One radical solution is to replace physical wiring with broadcasting of signals between the artificial neurons. We explore an implementation of this concept by light emitting/receiving III-V semiconductor nanowire neurons in a quasi-2D waveguide. They broadcast light in anisotropic patterns and specific regions in the nanowires are sensitised to exciting and inhibiting light signals. Weights of connections between nodes can then be tailored using the geometric light absorption/emission patterns. Through detailed simulations, we determine the connection strength based on rotation and separation between the nanowires. Our findings reveal that complex weight distributions are possible, indicating that specific neuron geometric patterns can achieve highly variable connectivity as needed for neural networks. An important design parameter is matching the wavelength to the specific physical dimensions of the network. To demonstrate applicability, we simulate a reservoir neural network using a hexagonal pattern of nanowires with random angular orientations, displaying its ability to perform chaotic time series prediction. The design is compatible with integration on Si substrates and can be extended to other nanophotonic components.</p>}},
  author       = {{Draguns, Kristians and Flodgren, Vidar and Winge, David and Serafini, Alfredo and Atvars, Aigars and Alnis, Janis and Mikkelsen, Anders}},
  issn         = {{2192-8614}},
  keywords     = {{III-V; nanowires; optical neural networks; semiconductors}},
  language     = {{eng}},
  number       = {{15}},
  pages        = {{2575--2585}},
  publisher    = {{De Gruyter}},
  series       = {{Nanophotonics}},
  title        = {{Neural network connectivity by optical broadcasting between III-V nanowires}},
  url          = {{http://dx.doi.org/10.1515/nanoph-2025-0035}},
  doi          = {{10.1515/nanoph-2025-0035}},
  volume       = {{14}},
  year         = {{2025}},
}