Jove
Visualize
Contact Us

Related Concept Videos

Biasing of FET01:22

Biasing of FET

263
Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
In an N-channel JFET, the structure consists of N-type material forming the channel on a P-type substrate, with the...
263
Voltammetry: Factors Affecting Measurements01:21

Voltammetry: Factors Affecting Measurements

151
A current produced due to the redox reactions of the analyte at the working and auxiliary electrodes is called a faradaic current. The reaction can be divided into two types. The current generated due to the reduction of the analyte is called cathodic current, and it carries a positive charge. In contrast, the current produced by analyte oxidation is known as an anodic current, and it has a negative charge. The applied potential at the working electrode determines the faradaic current flow, and...
151
Characteristics of MOSFET01:17

Characteristics of MOSFET

367
Metal-oxide-semiconductor field-effect Transistors, or MOSFETs, play a critical role in electronic circuits. They are primarily utilized for amplifying and switching signals.
Various vital parameters influence their functionality, which is crucial for theory and electronics applications. First, channel dimensions, precisely length, and width, are pivotal. The size of these channels affects the transistor's ability to carry current and switching speeds; shorter channels typically enable...
367

You might also read

Related Articles

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

Sort by
Same author

Analytical Solution for the Potential Distribution in the Channel of A Graphene Field-Effect Transistor Validated with a Custom-Fabricated Test Platform.

ACS applied electronic materials·2026
Same author

The growing memristor industry.

Nature·2025
Same author

Kinetic Monte Carlo simulation analysis of the conductance drift in Multilevel HfO<sub>2</sub>-based RRAM devices.

Nanoscale·2024
Same author

Hysteresis in memristors produces conduction inductance and conduction capacitance effects.

Physical chemistry chemical physics : PCCP·2024
Same author

Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors.

ACS applied electronic materials·2024
Same author

Hardware implementation of memristor-based artificial neural networks.

Nature communications·2024
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 Experiment Video

Updated: Jun 26, 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

4.1K

Variability in HfO2-based memristors described with a new bidimensional statistical technique.

C Acal1, D Maldonado2,3, A Cantudo2

  • 1Departamento de Estadística e Investigación Operativa e Instituto de Matemáticas (IMAG), Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain.

Nanoscale
|May 20, 2024
PubMed
Summary

A new statistical method uses 2D analysis to better understand variability in resistive switching memories. This approach offers a more comprehensive assessment for applications like non-volatile memory and neuromorphic computing.

More Related Videos

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

20.3K
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

Related Experiment Videos

Last Updated: Jun 26, 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

4.1K
Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

20.3K
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

Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Resistive switching (RS) memories are crucial for advanced computing applications.
  • Understanding cycle-to-cycle variability is essential for reliable device performance.
  • Current analysis methods often lack the granularity to fully capture complex variability.

Purpose of the Study:

  • To introduce a novel statistical analysis for assessing cycle-to-cycle variability in resistive memories.
  • To enhance the understanding of parameter distributions and their variation.
  • To provide a more robust method for evaluating memory technology suitability.

Main Methods:

  • Employing two-dimensional (2D) distributions to analyze set/reset voltages and currents.
  • Utilizing a 2D coefficient of variation (CV) for comprehensive data assessment.
  • Applying the methodology to resistive switching data from hafnium oxide-based technologies.

Main Results:

  • The 2D methodology provides a more thorough analysis compared to traditional 1D methods.
  • Demonstrated effectiveness in analyzing variability in hafnium oxide resistive memory devices.
  • The 2D CV offers a compact yet powerful metric for variability assessment.

Conclusions:

  • The proposed 2D statistical analysis significantly improves the assessment of resistive memory variability.
  • This method facilitates a more accurate evaluation of technology readiness for non-volatile memories, neuromorphic computing, and random number generation.
  • Enhanced understanding of variability is key to advancing next-generation electronic devices.