Neural network connectivity by optical broadcasting between III-V nanowires
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 co...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-07-01
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| Series: | Nanophotonics |
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| Online Access: | https://doi.org/10.1515/nanoph-2025-0035 |
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| _version_ | 1849390226182504448 |
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| author | Draguns Kristians Flodgren Vidar Winge David Serafini Alfredo Atvars Aigars Alnis Janis Mikkelsen Anders |
| author_facet | Draguns Kristians Flodgren Vidar Winge David Serafini Alfredo Atvars Aigars Alnis Janis Mikkelsen Anders |
| author_sort | Draguns Kristians |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d2712c49e2454b38becafcc4d4fca7ef |
| institution | Kabale University |
| issn | 2192-8614 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj-art-d2712c49e2454b38becafcc4d4fca7ef2025-08-20T03:41:43ZengDe GruyterNanophotonics2192-86142025-07-0114152575258510.1515/nanoph-2025-0035Neural network connectivity by optical broadcasting between III-V nanowiresDraguns Kristians0Flodgren Vidar1Winge David2Serafini Alfredo3Atvars Aigars4Alnis Janis5Mikkelsen Anders6University of Latvia, Riga, Latvia5193Lund University, Lund, Sweden5193Lund University, Lund, Sweden5193Lund University, Lund, SwedenUniversity of Latvia, Riga, LatviaUniversity of Latvia, Riga, Latvia5193Lund University, Lund, SwedenBiological 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.https://doi.org/10.1515/nanoph-2025-0035optical neural networksnanowiresiii-vsemiconductors |
| spellingShingle | Draguns Kristians Flodgren Vidar Winge David Serafini Alfredo Atvars Aigars Alnis Janis Mikkelsen Anders Neural network connectivity by optical broadcasting between III-V nanowires Nanophotonics optical neural networks nanowires iii-v semiconductors |
| title | Neural network connectivity by optical broadcasting between III-V nanowires |
| title_full | Neural network connectivity by optical broadcasting between III-V nanowires |
| title_fullStr | Neural network connectivity by optical broadcasting between III-V nanowires |
| title_full_unstemmed | Neural network connectivity by optical broadcasting between III-V nanowires |
| title_short | Neural network connectivity by optical broadcasting between III-V nanowires |
| title_sort | neural network connectivity by optical broadcasting between iii v nanowires |
| topic | optical neural networks nanowires iii-v semiconductors |
| url | https://doi.org/10.1515/nanoph-2025-0035 |
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