Hierarchical Serpentine-like Organic Crystal Optical Waveguides for Artificial Neural Networks
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915097180372992 |
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| author | Kumar, Avulu Vinod Rohullah, Mehdi Chosenyah, Melchi Gaddam, Sinduja Chandrasekar, Rajadurai |
| author_facet | Kumar, Avulu Vinod Rohullah, Mehdi Chosenyah, Melchi Gaddam, Sinduja Chandrasekar, Rajadurai |
| contents | Optical components and circuits that deal with multiple signal generation and processing are quintessential for artificial neural networks. Herein, we present a proof-of-concept four-layered organic optical artificial neural network (ANN)-like architecture, constructed from flexible organic crystals of (E)-1-(((5-methylpyridin-2-yl)imino)methyl)naphthalene-2-ol (MPyIN), employing an atomic force microscopy cantilever tip-based mechanical micromanipulation technique. Initially, the strategic selection of four MPyIN crystal active waveguides of varying lengths, mechanically bending them into serpentine-like forms, followed by their hierarchical integration, creates neuron-like, four-layered interconnected optical waveguides with six optical synapses. The synapses in the ANN-like architecture enable parallel transmissions of passive optical signals via evanescent coupling across multiple paths through various layers of the serpentine-shaped optical waveguides. Notably, the feedforward mechanism allows the synapses to multiply and split the optical signal generated at any input into four diverging signals with varying magnitudes. Here, certain outputs deliver a mixed signal (passive and active) due to diverging and converging optical transmission paths. This hierarchical, ANN-like tiny architecture paves the way for the development of smart optical neural networks utilizing multiple emissive and phase-changing organic crystals. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_05831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Serpentine-like Organic Crystal Optical Waveguides for Artificial Neural Networks Kumar, Avulu Vinod Rohullah, Mehdi Chosenyah, Melchi Gaddam, Sinduja Chandrasekar, Rajadurai Optics Disordered Systems and Neural Networks Materials Science Optical components and circuits that deal with multiple signal generation and processing are quintessential for artificial neural networks. Herein, we present a proof-of-concept four-layered organic optical artificial neural network (ANN)-like architecture, constructed from flexible organic crystals of (E)-1-(((5-methylpyridin-2-yl)imino)methyl)naphthalene-2-ol (MPyIN), employing an atomic force microscopy cantilever tip-based mechanical micromanipulation technique. Initially, the strategic selection of four MPyIN crystal active waveguides of varying lengths, mechanically bending them into serpentine-like forms, followed by their hierarchical integration, creates neuron-like, four-layered interconnected optical waveguides with six optical synapses. The synapses in the ANN-like architecture enable parallel transmissions of passive optical signals via evanescent coupling across multiple paths through various layers of the serpentine-shaped optical waveguides. Notably, the feedforward mechanism allows the synapses to multiply and split the optical signal generated at any input into four diverging signals with varying magnitudes. Here, certain outputs deliver a mixed signal (passive and active) due to diverging and converging optical transmission paths. This hierarchical, ANN-like tiny architecture paves the way for the development of smart optical neural networks utilizing multiple emissive and phase-changing organic crystals. |
| title | Hierarchical Serpentine-like Organic Crystal Optical Waveguides for Artificial Neural Networks |
| topic | Optics Disordered Systems and Neural Networks Materials Science |
| url | https://arxiv.org/abs/2501.05831 |