Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Design Example: Capacitance Multiplier Circuit01:20

Design Example: Capacitance Multiplier Circuit

704
In integrated circuit technology, a capacitance multiplier is often utilized to produce a larger capacitance value when a small physical capacitance falls short. This is achieved by a circuit that multiplies capacitance values by a factor of up to 1000, such that a 10-pF capacitor can replicate the performance of a 100-nF capacitor.
The circuit illustrated in Figure 1 below incorporates two op-amps, with the first operating as a voltage follower and the second acting as an inverting amplifier.
704
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

544
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...
544

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Localized carbon deposition enables trimming of photonic integrated circuits.

Nature communications·2026
Same author

Nonvolatile photonic field-programmable coupler array.

Science advances·2026
Same author

Deep neural network inference on an integrated, reconfigurable photonic tensor processor.

Nature communications·2026
Same author

Reconfigurable, Temperature Resilient Phase-Change Metasurfaces Fabricated via High Throughput Nanoimprinting Lithography.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Wide Angle Polarization-Independent 6-Bit Optical Modulator Using Phase Change Material.

Nano letters·2026
Same author

All-optical temporal integration mediated by subwavelength heat antennas.

Nature communications·2025
Same journal

Recent Progress in on-Demand Transfer-Enabled Integration of Wavelength-Scale Light Sources.

Nanophotonics (Berlin, Germany)·2026
Same journal

Tunable skyrmion bag textures in surface phonon polariton lattices.

Nanophotonics (Berlin, Germany)·2026
Same journal

All-Optical Diffractive Operators for Rapid, Computer-Free Morphological Transformations.

Nanophotonics (Berlin, Germany)·2026
Same journal

Tunable Skyrmion, Meron, and Skyrmion Bag Textures in Surface Phonon Polariton Lattices.

Nanophotonics (Berlin, Germany)·2026
Same journal

Deep-Subwavelength Slot-Enhanced Broadband Dynamic Camouflage Metasurface Across the S, C, X, and Ku Bands.

Nanophotonics (Berlin, Germany)·2026
Same journal

Machine Learning-Driven Cooling Window Design Beyond Hyperbolic Metamaterials.

Nanophotonics (Berlin, Germany)·2026
查看所有相关文章

相关实验视频

Updated: Jun 5, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

486

一个大规模的光子矩阵处理器,通过电荷积累来实现.

Frank Brückerhoff-Plückelmann1,2, Ivonne Bente3,2, Daniel Wendland3,2

  • 1Department of Physics, University of Münster, CeNTech, Heisenberg Str. 11, 48155 Muenster, Germany.

Nanophotonics (Berlin, Germany)
|December 5, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种用于人工神经网络 (ANN) 的时间复合光子电路. 这种方法增强了矩阵处理能力,使复杂的AI任务能够进行高效的大规模计算.

关键词:
矩阵向量的乘法乘法.这是光子计算.时间复杂化 (time-multiplexing) 是一种时间复杂化.

更多相关视频

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

8.9K
Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.0K

相关实验视频

Last Updated: Jun 5, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

486
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

8.9K
Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.0K

科学领域:

  • 神经形态光子学 神经形态光子学
  • 人工智能硬件是人工智能的硬件.

背景情况:

  • 光子电路为人工神经网络 (ANN) 提供了能源和时间的效率,因为它具有高带宽和低损失.
  • 扩展当前光子电路以满足现代ANN的需求仍然是一个重大挑战.

研究的目的:

  • 为解决ANN现有的光子矩阵处理器的缩放限制.
  • 提出和研究一种新的时间复数矩阵处理方案.

主要方法:

  • 在ANN中对矩阵大小的概述和与现有的光子矩阵处理器能力的比较.
  • 关于使用不连贯光学积累的时间复杂矩阵处理方案的建议和研究.
  • 通过1个小时的脉冲实现了98.9%的积累精度.

主要成果:

  • 拟议的方案实际上增加了物理光子横杆阵列的尺寸,而无需电气后处理.
  • 在时间复合光学积累方面,已证明高积累精度 (98.9%).
  • 预计在51.2mm2面积上实现16000×64矩阵的全光矩阵向量乘法的能力.

结论:

  • 时间复合的神经形态光子电路架构使ANN能够进行高效的大规模矩阵操作.
  • 这种方法可实现每秒超过110万亿次的多倍累积运算,克服了当前的扩展挑战.