Photonic Neural Networks: A Compact Review

TL;DR

This paper reviews the development of photonic neural networks, focusing on experimental, theoretical, and mathematical principles, highlighting recent advances, simulation methods, and key parameters in this emerging interdisciplinary field.

Contribution

It provides a comprehensive review of 18 key articles from 2015 to 2022, categorizing principles and emphasizing the role of mathematics and simulation in photonic neural networks.

Findings

Photonic neural networks leverage light for high-speed, low-precision computations.

Mathematical and simulation tools are crucial for advancing photonic neural network research.

Recent studies demonstrate significant progress in experimental and theoretical understanding.

Abstract

It has long been known that photonic science and especially photonic communications can raise the speed of technologies and producing manufacturing. More recently, photonic science has also been interested in its capabilities to implement low-precision linear operations, such as matrix multiplications, fast and effciently. For a long time most scientists taught that Electronics is the end of science but after many years and about 35 years ago had been understood that electronics do not answer alone and should have a new science. Today we face modern ways and instruments for doing tasks as soon as possible in proportion to many decays before. The velocity of progress in science is very fast. All our progress in science area is dependent on modern knowledge about new methods. In this research, we want to review the concept of a photonic neural network. For this research was selected 18 main articles were among the main 30 articles on this subject from 2015 to the 2022 year. These articles noticed three principles: 1- Experimental concepts, 2- Theoretical concepts, and, finally 3- Mathematic concepts. We should be careful with this research because mathematics has a very important and constructive role in our topics! One of the topics that are very valid and also new, is simulation. We used to work with simulation in some parts of this research. First, briefly, we start by introducing photonics and neural networks. In the second we explain the advantages and disadvantages of a combination of both in the science world and industries and technologies about them. Also, we are talking about the achievements of a thin modern science. Third, we try to introduce some important and valid parameters in neural networks. In this manner, we use many mathematic tools in some portions of this article.