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Encontradas 2 publicações
Temas Prioritários: Fotônica Integrada e Tecnologias Quânticas2026
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2026 26th International Conference on Transparent Optical Networks (ICTON)

Photonic Computing Towards Energy-Efficient AI Solutions Based on Photoactivated Materials

DIAS, Lília M. S.; BASTOS, Ana R.; FU, Lianshe; FERREIRA, Rute A. S.; ANDRÉ, Paulo S. B.

Tipos de publicação: Anais de Congresso, Paper

Artificial intelligence (AI) and neuromorphic computing are driving the development of hardware platforms that combine high energy efficiency with physically informed data processing. Photonic technologies offer distinct advantages, particularly when coupled with photoactivated materials that allow light-matter interactions to be directly exploited for computation. Inspired by synaptic mechanisms in biological systems, a neuromorphic photonic layer based on a luminescent material was developed to mimic synaptic memory via the material’s optical afterglow. The information-processing capability of the photonic front-end was evaluated by coupling its output responses to a digital machine learning model, achieving 76.9% test accuracy on the MNIST handwritten digit recognition task. This approach demonstrates the potential of hardware AI systems that tightly integrate material-level functionalities with neuromorphic computation by exploiting synapse-like optical memory in luminescent materials.

#Artificial synapse#luminescence#neuromorphic engineering#optical computing
Temas Prioritários: Fotônica Integrada e Tecnologias Quânticas2026
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Neuromorphic Computing and Engineering

Mobile-compatible neuromorphic optical computing enabled by dual-emission photonic materials

DIAS, Lília M. S.; BASTOS, Ana R.; FU, Lianshe; CARNEIRO NETO, Albano N.; PEREIRA, Rui F. P.; BERMUDEZ, Verónica de Zea; TOWE, Elias; FERREIRA, Rute A. S.; ANDRÉ, Paulo S. B.

Tipos de publicação: Artigo

Artificial intelligence (AI) and neuromorphic computing demand hardware platforms that combine energy efficiency with physically informed data processing. Photonics offers unique advantages in this context, but optical neuromorphic systems in which memory and information processing arise from intrinsic material dynamics remain scarce. We demonstrate a photonic layer in which history-dependent material photophysics implements physically embedded information processing before digital learning, using luminescent phosphors. The photonic layer exhibits dual fluorescence and phosphorescence together with excitation-history-dependent photoactivation dynamics that emulate synaptic functionalities, including short-term memory, long-term memory, and synaptic potentiation. Quantitative analysis and modelling reveal efficient nanoscale interlamellar energy transfer consistent with the lamellar material morphology, establishing a link between structure and function. When integrated as an active optical front-end within a hybrid photonic–digital AI architecture, the photonic layer performs a material-based transformation of input data before digital learning. Using a laboratory-based optical readout, classification accuracy comparable to state-of-the-art hybrid neuromorphic computing (approximately 94%) is achieved while requiring fewer training epochs. A mobile-compatible implementation based on smartphone optical readout yields similar accuracy, demonstrating robustness to non-specialized optical hardware. These results outline a general strategy for neuromorphic photonic systems in which functional materials act as adaptive computational primitives, enabling energy-efficient computing architectures.

#afterglow#luminescence#mobile optical computing#neuromorphic optical computing#photonic materials#synaptic functionalities