The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nanophotonics, which is the study of the behaviour of light and the light-matter interaction at the nanometre scale, has unveiled new phenomena and led to new applications beyond the diffraction limit of light. These emerging nanophotonic devices have enabled scientists to develop paradigm shifts of research into ANNs. In the present review, we summarise the recent progress in nanophotonics for emulating the structural, functional and biological features of ANNs, directly or indirectly.

/pdf/artificial-neural-networks-enabled-by-nanophotonics-59npxozlwk.pdf

Artificial neural networks enabled by nanophotonics.

With the explosive growth of mathematical optimization and computing hardware, deep neural networks (DNN) have become tremendously powerful tools to solve many challenging problems in various fields, ranging from decision making to computational imaging and holography. In this manuscript, I focus on the prosperous interactions between DNN and holography. On the one hand, DNN has been demonstrated to be in particular proficient for holographic reconstruction and computer-generated holography almost in every aspect. On the other hand, holography is an enabling tool for the optical implementation of DNN the other way around owing to the capability of interconnection and light speed processing in parallel. The purpose of this article is to give a comprehensive literature review on the recent progress of deep holography, an emerging interdisciplinary research field that is mutually inspired by holography and DNN. I first give a brief overview of the basic theory and architectures of DNN, and then discuss some of the most important progresses of deep holography. I hope that the present unified exposition will stimulate further development in this promising and exciting field of research. 

Deep holography

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076079

Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Television holography systems are considered with particular emphasis on the resolution requirements of the camera and reproducer. Methods are shown whereby conventional cameras and reproducers can...

http://iopscience.iop.org/0029-4780/1/2/4104/pdf/0029-4780_1_2_4104.pdf

Considerations of television holography

Digital neural networks, matched filters and optical implementations

While numerous artificial neural network (ANN) models have been electronically implemented and simulated by conventional computers, optical technology provides a far superior mechanism for the implementation of large-scale ANNs. The properties of light make it an ideal carrier of data signals. With optics, very large and high speed neural network architectures are possible. Because light is a predictable phenomenon, it can be described mathematically and its behavior can be simulated by conventional computers. A hologram is in essence a capture of the light field at a particular moment in time and space. Later, the hologram can be used to reconstruct the three dimensional light field carrying optical data. This makes a hologram an ideal medium for capturing, storing, and transmitting data in optical computers, such as optical neural networks (ONNs). Holograms can be created using conventional methods, but they can also be computer generated. In this paper, we will present an overview of optical neural networks, with emphasis on the holographic neural networks. We will take a look at the mathematical basis of holography in terms of the Fresnel Zone Plate and how it can be utilized in making computer generated holograms (CGHs). Finally, we will present various methods of CGH implementation in a two layer holographic ONN.

F. Loochan

Papers

Computer Generated Holograms for Optical Neural Networks