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Abstract Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: (i) deriving device behavior from design parameters, (ii) simulating device performance, (iii) finding the optimal candidate designs from simulations, (iv) fabricating the optimal device, and (v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems. 

Abstract The manipulation of magnetization through optically generated ultrafast spin currents is a fascinating area that needs a thorough understanding for its potential future applications. In this work, a comprehensive investigation of helicity‐driven optical spin‐orbit torque in heavy metal/ferromagnetic metal heterostructures is presented, specifically cobalt capped with gold or platinum, subject to laser pumping at different wavelengths. The results demonstrate up to tenfold enhancement in optical spin‐orbit torque quantum efficiency for gold compared to platinum of the same thickness when pumped with a visible laser. Additionally, the study provides the first experimental analysis of the photon energy dependence of optical spin‐orbit torque and derives the optical spin orientation spectra for both gold/cobalt and platinum/cobalt heterostructures. A key insight gained from the study is the impact of photon energy‐dependent spin transport in the system, which suggests the use of a high photon energy pump for efficient spin transport. These findings highlight the potential of spin current generation and manipulation in gold/ferromagnet heterostructures for a wide range of applications such as all‐optical magnetization switching, spin‐wave generation and control, and spintronic terahertz emission. 

Machine learning with artificial neural networks has recently transformed many scientific fields by introducing new data analysis and information processing techniques. Despite these advancements, efficient implementation of machine learning on conventional computers remains challenging due to speed and power constraints. Optical computing schemes have quickly emerged as the leading candidate for replacing their electronic counterparts as the backbone for artificial neural networks. Some early integrated photonic neural network (IPNN) techniques have already been fast-tracked to industrial technologies. This review article focuses on the next generation of optical neural networks (ONNs), which can perform machine learning algorithms directly in free space. We have aptly named this class of neural network model the free space optical neural network (FSONN). We systematically compare FSONNs, IPNNs, and the traditional machine learning models with regard to their fundamental principles, forward propagation model, and training process. We survey several broad classes of FSONNs and categorize them based on the technology used in their hidden layers. These technologies include 3D printed layers, dielectric and plasmonic metasurface layers, and spatial light modulators. Finally, we summarize the current state of FSONN research and provide a roadmap for its future development. 

Non-Hermitian meta-gratings enable unidirectional excitation and reflection of optical surface waves at the nanoscale.