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

MOS Capacitor01:25

MOS Capacitor

619
A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
619
The Resting Membrane Potential01:21

The Resting Membrane Potential

126.3K
Overview
126.3K
Resting Membrane Potential01:24

Resting Membrane Potential

17.5K
The relative difference in electrical charge, or voltage, between the inside and the outside of a cell membrane, is called the membrane potential. It is generated by differences in permeability of the membrane to various ions and the concentrations of these ions across the membrane.
The Inside of a Neuron is More Negative
The membrane potential of a cell can be measured by inserting a microelectrode into a cell and comparing the charge to a reference electrode in the extracellular fluid. The...
17.5K

You might also read

Related Articles

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

Sort by
Same author

A bibliometric analysis of lung cancer and atrial fibrillation [1991-2026]: research hotspots, thematic evolution, and translational landscape in cardio-oncology.

Journal of thoracic disease·2026
Same author

Association between amygdala subregions and non-motor symptoms in Parkinson's disease: a fixel-based analysis.

Brain imaging and behavior·2026
Same author

Identification of protein lysine lactylation and potential targets in esophageal squamous cell carcinoma.

Translational cancer research·2026
Same author

PathRWKV: Enhancing Whole Slide Image Inference with Asymmetric Recurrent Modeling.

IEEE transactions on medical imaging·2026
Same author

Integrative pan-cancer analysis of transferrin reveals context-dependent prognostic associations and links to immune and metabolic disease-related programs.

Discover oncology·2026
Same author

Lateral flow immunoassay based on electrospun cellulose acetate nanofibrous membrane for C-reactive protein.

Talanta·2026

Related Experiment Video

Updated: May 9, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

7.7K

Mortise-tenon-shaped memristors for scientific computing.

Weiqi Dang1, Yu Shen1, Wei Wei1

  • 1Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Jiangsu Physical Science Research Center, Nanjing 210093, China.

Science Advances
|April 30, 2025
PubMed
Summary

Researchers developed a novel mortise-tenon-shaped memristor for in-memory computing. This innovation significantly improves uniformity and accelerates scientific computations, offering a path to more efficient hardware.

More Related Videos

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

8.9K
In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
09:49

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

Published on: May 13, 2020

4.0K

Related Experiment Videos

Last Updated: May 9, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

7.7K
A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

8.9K
In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
09:49

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

Published on: May 13, 2020

4.0K

Area of Science:

  • Materials Science
  • Computer Engineering
  • Computational Science

Background:

  • In-memory computing using memristors offers parallel processing for scientific computing.
  • Memristor nonuniformity necessitates complex peripheral circuits, increasing power consumption and limiting practical applications.

Purpose of the Study:

  • To address memristor nonuniformity and enhance in-memory computing efficiency for scientific applications.
  • To develop a novel memristor structure with improved uniformity and performance.

Main Methods:

  • Fabrication of a mortise-tenon-shaped (MTS) memristor by integrating a mortise-shaped hexagonal boron nitride (h-BN) flake onto a HfO2 switching layer.
  • Characterization of MTS memristor uniformity, including cycle-to-cycle and device-to-device variations.
  • Implementation of MTS memristors in a partial differential equation solver to evaluate performance.

Main Results:

  • The MTS memristor demonstrated ultrahigh uniformity with significantly reduced cycle-to-cycle (~2.5%) and device-to-device (~6.9%) variations compared to standard HfO2 memristors.
  • A partial differential equation solver built with MTS memristors achieved a five-times faster convergence speed for solving the Poisson equation than traditional memristor-based solvers.

Conclusions:

  • The MTS memristor structure effectively mitigates nonuniformity issues in memristor-based in-memory computing.
  • This approach offers a promising solution for reducing hardware resources and enhancing the speed and accuracy of scientific computing.