The Optic Brain: foundations, frontiers, and the future of photonic artificial intelligence

Abstract

Optical Neural Networks (ONNs) are emerging as a revolutionary approach in computing, utilizing the unique advantages of light to achieve high-speed and energy-efficient data processing. This paper presents a comprehensive review of ONNs, detailing their architecture, operational principles, and recent technological advancements. ONNs’ parallelism and low latency make them well-suited for real-time image recognition and large-scale ML. The study examines various implementation methods, including diffractive deep neural networks and photonic integrated circuits, and highlights innovations using nanophotonic components that enable compact and trainable optical models. Despite their potential, ONNs face significant challenges, particularly in implementing optical nonlinearity, mitigating noise sensitivity, and achieving seamless integration with electronic control systems. To address these limitations, the paper explores promising solutions such as hybrid electro-optic platforms and engineered meta-materials. A comparative evaluation between optical and traditional electronic neural networks reveals important trade-offs in performance and deployment feasibility. Although ONNs are not yet positioned to fully replace electronic systems, they offer substantial advantages in specific domains where speed and power efficiency are critical. Ultimately, the continued convergence of photonics, materials science, and artificial intelligence research will be key to unlocking the full potential of optical computing.