Intelligent optoelectronic processor for orbital angular momentum spectrum measurement

Abstract

Abstract Orbital angular momentum (OAM) detection underpins almost all aspects of vortex beams’ advances such as communication and quantum analogy. Conventional schemes are frustrated by low speed, complicated system, limited detection range. Here, we devise an intelligent processor composed of photonic and electronic neurons for OAM spectrum measurement in a fast, accurate and direct manner. Specifically, optical layers extract invisible topological charge information from incoming light and a shallow electronic layer predicts the exact spectrum. The integration of optical-computing promises us a compact single-shot system with high speed and energy efficiency (optical operations / electronic operations ~ $${10}^{3}$$ 10 3 ), neither necessitating reference wave nor repetitive steps. Importantly, our processor is endowed with salient generalization ability and robustness against diverse structured light and adverse effects (mean squared error ~ $$10^{(-5)}$$ 10 ( - 5 ) ). We further raise a universal model interpretation paradigm to reveal the underlying physical mechanisms in the hybrid processor, as distinct from conventional ‘black-box’ networks. Such interpretation algorithm can improve the detection efficiency up to 25-fold. We also complete the theory of optoelectronic network enabling its efficient training. This work not only contributes to the explorations on OAM physics and applications, and also broadly inspires the advanced links between intelligent computing and physical effects.

Keywords

• Orbital angular momentum spectrum
• Deep learning
• Optoelectronic neural networks
• Optical computing
• Diffractive deep neural networks