Photonic Computing Towards Energy-Efficient AI Solutions Based on Photoactivated Materials
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.
Tunable multicomponent emission from carbon nanomaterials for neuromorphic optical synaptic functions
Multi-component luminescent materials provide an effective route to engineer complex optical responses and temporal emission dynamics for neuromorphic photonics. In this work, we present a neuromorphic photonic layer based on a multi-component carbon-based nanomaterial (THR@BA) composed of a rigid boron oxide matrix derived from the calcination of boric acid (BA), which embeds three organic molecules, terephthalic acid (TPA), 1-hydroxy-2-naphthoic acid (HNA) and rhodamine B (RhB), forming a confined host-guest system with enhanced intermolecular interactions. The rigid matrix promotes energy transfer and excited-state coupling between the molecular components. The multi-component nanomaterial exhibits dual emission of fluorescence and long-lived phosphorescence and simultaneously provides complex responses upon multi-wavelength excitation. These responses result from the superposition of the optical properties of each single-component nanomaterial functionalized with the respective organic molecule, namely TPA@BA, HNA@BA, and RhB@BA. By tuning the excitation parameters, THR@BA reproduces key synaptic functions, including short- and long-term memory as well as potentiation involved in synaptic plasticity, deriving from the dynamic balance between fluorescence and phosphorescence emission with lifetimes ranging from nanoseconds to seconds. This work demonstrates that multi-component molecular systems confined within simple inorganic matrices can serve as versatile platforms for engineering programmable luminescence dynamics, making them ideal candidates for artificial optical synapses and neuromorphic computing.