paper-with-me

홈 › Papers

Photonic Neural Networks: A Compact Review

2023-02-16 · Mohammad Ahmadi, Hamidreza Bolhasani

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.

📄 PDF Abstract BibTeX arXiv:2302.08390

Code (0)

등록된 구현이 없습니다.

Tasks

Articlesvalid

Methods 이 논문이 사용한 방법론

SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…

Similar Papers 제목 키워드 기반

Machine Learning enables Ultra-Compact Integrated Photonics through Silicon-Nanopattern Digital Metamaterials

2020-11-23 · Sourangsu Banerji, Apratim Majumder, Alex Hamrick, Rajesh Menon 외

In this work, we demonstrate three ultra-compact integrated-photonics devices, which are designed via a machine-learning algorithm coupled with finite-difference time-domain (FDTD) modeling. Through digitizing the design…

BIG-bench Machine Learning

Photonic Quantum Computers

2024-09-12 · M. AbuGhanem

In the pursuit of scalable and fault-tolerant quantum computing architectures, photonic-based quantum computers have emerged as a leading frontier. This article provides a comprehensive overview of advancements in photon…

Harnessing Photonics for Machine Intelligence

2026-04-12 · Hanqing Zhu, Shupeng Ning, Hongjian Zhou, Ziang Yin 외 arxiv

The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integ…

Data driven approaches in nanophotonics: A review of AI-enabled metadevices

2025-09-30 · Huanshu Zhang, Lei Kang, Sawyer D. Campbell, Jacob T. Young 외 arxiv

Data-driven approaches have revolutionized the design and optimization of photonic metadevices by harnessing advanced artificial intelligence methodologies. This review takes a model-centric perspective that synthesizes …

A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design

2026-08-18 · Huanshu Zhang, Kegeng Tang, Lei Kang, Sawyer D. Campbell 외 arxiv

Metasurfaces have revolutionized the development of photonic devices by enabling unprecedented precision in light manipulation. However, their design processes are often constrained by computationally expensive simulatio…

Code Generation