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Both two-dimensional (2D) transitional metal dichalcogenides (TMDs) and III–V semiconductors have been considered as potential platforms for quantum technology. While 2D TMDs exhibit a large exciton binding energy, and their quantum properties can be tailored via heterostructure stacking, TMD technology is currently limited by the incompatibility with existing industrial processes. Conversely, III-nitrides have been widely used in light-emitting devices and power electronics but not leveraging excitonic quantum aspects. Recent demonstrations of 2D III-nitrides have introduced exciton binding energies rivaling TMDs, promising the possibility to achieve room-temperature quantum technologies also with III-nitrides. Here, we discuss recent advancements in the synthesis and characterizations of 2D III-nitrides with a focus on 2D free-standing structures and embedded ultrathin quantum wells. We overview the main obstacles in the material synthesis, vital solutions, and the exquisite optical properties of 2D III-nitrides that enable excitonic and quantum-light emitters. 

Abstract Recent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector arrays, and the rapid expansion of the capabilities of machine learning including the development of powerful neural networks. This paper demonstrates 3D tracking of point-like objects with multilayer feedforward neural networks and the extension to tracking positions of multi-point objects. Computer simulations further demonstrate how this optical system can track extended objects in 3D, highlighting the promise of combining nanophotonic devices, new optical system designs, and machine learning for new frontiers in 3D imaging.