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

1.0K
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...
1.0K
Mnemonic Devices01:23

Mnemonic Devices

199
Mnemonic devices are cognitive tools that facilitate memory retention by linking new information to familiar patterns or organizational strategies. These techniques are beneficial for remembering complex or lengthy sets of information by simplifying and structuring them in easily retrievable ways.
Acronyms
Acronyms are created by using the initial letters of a series of words to form a new word or phrase. This approach condenses complex information into a single, memorable entity. For example,...
199
System of Memory01:23

System of Memory

6.6K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
6.6K
Non-ohmic Devices00:51

Non-ohmic Devices

1.2K
In most substances, the current flow is proportional to the voltage applied to it. A simple relationship between the values of current, voltage, and resistance is known as Ohm's law. Nonohmic devices do not exhibit a linear relationship between voltage and current. One such device is the semiconducting circuit element known as a diode. A diode is a circuit device that allows current flow in only one direction.
Consider a simple circuit consisting of a battery, a diode, and a resistor. A...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Association of intensive blood pressure lowering after endovascular thrombectomy with outcomes according to contrast staining ASPECTS.

Stroke and vascular neurology·2026
Same author

Morphology-Embedded Signatures of Lattice Strain in Ferroelectric BaTiO<sub>3</sub> Thin Films Revealed by Machine Learning.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

OpenDicomViewer: A Lightweight Open-Source DICOM Viewer for macOS Built with Swift.

Journal of imaging informatics in medicine·2026
Same author

Association between thrombus neutrophil extracellular trap content and ischemic stroke recurrence.

Journal of thrombosis and haemostasis : JTH·2026
Same author

Different Long-Term Outcomes According to Thrombus Histology in Patients With Acute Ischemic Stroke.

Journal of stroke·2026
Same author

Combined Oral Anticoagulant and Antiplatelet for Atrial Fibrillation and Cerebral Atherosclerosis: A Meta-Analysis.

Journal of stroke·2026

Related Experiment Video

Updated: Oct 6, 2025

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

4.2K

A crossbar array of magnetoresistive memory devices for in-memory computing.

Seungchul Jung1, Hyungwoo Lee1, Sungmeen Myung1

  • 1Samsung Advanced Institute of Technology, Samsung Electronics, Suwon-si, South Korea.

Nature
|January 13, 2022
PubMed
Summary

Researchers developed a novel 64x64 MRAM crossbar array for low-power artificial neural networks. This in-memory computing approach uses resistance summation, overcoming MRAM

More Related Videos

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

8.0K
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

9.1K

Related Experiment Videos

Last Updated: Oct 6, 2025

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

4.2K
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

8.0K
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

9.1K

Area of Science:

  • * Materials Science and Engineering
  • * Computer Engineering
  • * Artificial Intelligence

Background:

  • * Artificial neural networks (ANNs) require efficient multiply-accumulate operations, often implemented using in-memory computing with non-volatile memories.
  • * Spin-transfer-torque magnetoresistive random-access memory (MRAM) offers practical advantages but faces challenges in conventional crossbar arrays due to low resistance and high power consumption.
  • * Existing analogue in-memory computing approaches utilize resistive, phase-change, or flash memory, but MRAM integration remains difficult.

Purpose of the Study:

  • * To develop a low-power, high-performance crossbar array using MRAM for analogue artificial neural network acceleration.
  • * To overcome the limitations of MRAM's low resistance in conventional current-summation crossbar architectures.
  • * To demonstrate the efficacy of the MRAM crossbar array in various machine learning tasks.

Main Methods:

  • * Designed and fabricated a 64x64 crossbar array utilizing MRAM cells.
  • * Implemented a novel architecture employing resistance summation for analogue multiply-accumulate operations.
  • * Integrated the MRAM array with 28nm complementary metal-oxide-semiconductor (CMOS) readout electronics.

Main Results:

  • * Achieved 93.23% accuracy in classifying Modified National Institute of Standards and Technology (MNIST) digits using a two-layer perceptron (software baseline: 95.24%).
  • * Demonstrated improved accuracy of 98.86% in an emulation of an eight-layer Visual Geometry Group-8 (VGG-8) neural network with measured errors (software baseline: 99.28%).
  • * Successfully implemented face detection with 93.4% accuracy using a single layer of the neural network.

Conclusions:

  • * The developed MRAM crossbar array effectively addresses the low-resistance challenge through resistance summation, enabling efficient analogue multiply-accumulate operations.
  • * The MRAM-based in-memory computing architecture shows significant potential for low-power, high-performance artificial neural network implementations.
  • * This technology paves the way for practical, large-scale commercialization of MRAM in neuromorphic computing applications.