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.