Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

848
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
848
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.4K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.4K
Photoluminescence: Applications01:14

Photoluminescence: Applications

591
Photoluminescence offers a wide range of applications due to its inherent sensitivity and selectivity. This technique allows for both direct and indirect analyses of the analyte. Direct quantitative analysis is possible when the analyte exhibits a favorable quantum yield for fluorescence or phosphorescence. However, an indirect analysis may be feasible if the analyte is not fluorescent or phosphorescent, or if the quantum yield is unfavorable. Indirect methods include reacting the analyte with...
591

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Spatial heterogeneity, source apportionment and ecological risks of trace metal(loid)s in lakes across China.

Journal of hazardous materials·2026
Same author

An all-optical signal processor enabling terabit-per-second real-time equalization.

Science (New York, N.Y.)·2026
Same author

Programmable Three-dimensional Photonic Neural Network Chip.

Nature communications·2026
Same author

Central events in self-generated thoughts: Inferring short-term mental health status through narrative networks.

Applied psychology. Health and well-being·2026
Same author

Polystyrene nanoplastics and benzo(a)pyrene synergistically induce lung fibrosis and inflammation via relaxin signalling in mice.

Communications biology·2026
Same author

Effects of Lipopolysaccharide Challenge on Growth Performance and Immune Function of Yellow-Feathered Broilers.

Animals : an open access journal from MDPI·2026

Related Experiment Video

Updated: Oct 30, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

9.3K

Photonic Matrix Computing: From Fundamentals to Applications.

Junwei Cheng1, Hailong Zhou1, Jianji Dong1

  • 1Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Nanomaterials (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

Optical computing offers high-speed, energy-efficient solutions for artificial intelligence matrix operations. This review explores photonic matrix computing principles, optical neural networks, and future prospects.

Keywords:
artificial intelligencediffractive planesoptical neural networksphotonic acceleratorsphotonic integrated platformphotonic matrix computing

More Related Videos

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.3K
A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
07:56

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.7K

Related Experiment Videos

Last Updated: Oct 30, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

9.3K
Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.3K
A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
07:56

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.7K

Area of Science:

  • Photonics and Artificial Intelligence
  • Optical Computing Architectures

Background:

  • Emerging AI applications demand high-speed, energy-efficient computation for massive matrix operations.
  • Current electronic processors face limitations in meeting these demands.
  • Optical computing offers a promising alternative using photonic integrated platforms or free-space systems.

Purpose of the Study:

  • To review the principles of photonic matrix computing.
  • To survey the research progress of optical neural networks (ONNs) utilizing photonic matrix computing.
  • To discuss the advantages, challenges, and future prospects of optical computing in AI.

Main Methods:

  • Introduction to three mainstream schemes for photonic matrix computing.
  • Review of research advancements in optical neural networks.
  • Comparative analysis of optical versus electronic computing architectures.

Main Results:

  • Photonic matrix computing enables high-speed parallel processing with ultra-low energy consumption.
  • Optical neural networks show significant potential for AI acceleration.
  • Optical computing architectures present distinct advantages over electronic processors.

Conclusions:

  • Optical computing is a key technology for future AI advancements.
  • Addressing current challenges in optical computing will unlock its full potential.
  • Continued research promises innovative applications in high-performance computing.