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
Classification of Systems-I01:26

Classification of Systems-I

355
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
355
Understanding Memory01:19

Understanding Memory

679
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
679
Associative Learning01:27

Associative Learning

641
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
641
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
Long-Term Memory01:18

Long-Term Memory

294
Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
294

You might also read

Related Articles

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

Sort by
Same author

Temperature Directed Solid-State Sulfidation of MOF-Derived Bi<sub>2</sub>S<sub>3</sub>/N-Doped Carbon Composites for Enhanced Supercapacitor Performance.

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

Cerebellum-inspired memtransistors enable emergent differentiation for hardware-efficient novelty detection.

Nature communications·2026
Same author

Gate-Tunable Magnetoresistance in Antiferromagnetic van der Waals FePS<sub>3</sub> Transistors.

Nano letters·2026
Same author

The Role of Defect Geometry in Localized Emission from Monolayer Tungsten Dichalcogenides.

ACS nano·2026
Same author

Simulation-Guided Optimization of NH<sub>3</sub>/H<sub>2</sub> Cocombustion over a CuO Catalyst: Achieving High-Efficiency and near-Zero NO<sub><i>x</i></sub> Emissions.

Environmental science & technology·2026
Same author

Methanogenic recovery under organic overload using starfish-derived powder.

Frontiers in microbiology·2026

Related Experiment Video

Updated: Oct 6, 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

8.0K

Linear and Symmetric Li-Based Composite Memristors for Efficient Supervised Learning.

Su-Min Kim1, Sungkyu Kim2, Leo Ling3

  • 1Department of Materials Science & Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon, Gangwon24341, Korea.

ACS Applied Materials & Interfaces
|January 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Li-based composite memristor (LCM) for energy-efficient neuromorphic circuits. The LCM enables highly accurate image classification with fewer training steps, outperforming conventional memristors.

Keywords:
artificial neural networkscomposite memristorlithium titanatelithium-based memristorphase transition

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

9.1K
Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
06:28

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Published on: September 27, 2024

2.6K

Related Experiment Videos

Last Updated: Oct 6, 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

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
Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
06:28

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Published on: September 27, 2024

2.6K

Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Neuromorphic circuits leverage artificial neural networks (ANNs) for energy efficiency.
  • Memristors in crossbar arrays offer efficient ANN vector-matrix multiplication compared to CMOS.
  • Specific memristor characteristics, including linear potentiation/depression, are crucial for optimal performance.

Purpose of the Study:

  • To develop a memristor with characteristics suitable for advanced neuromorphic computing.
  • To demonstrate a Li-based composite memristor (LCM) that meets the requirements for efficient ANNs.

Main Methods:

  • Fabrication of a Li-based composite memristor (LCM) with Li-doped TiO2, Li4Ti5O12, and Li7Ti5O12 phases.
  • Characterization of the LCM's resistive switching behavior, focusing on symmetry and linearity.
  • Simulation of the LCM's performance in an artificial neural network for image classification.

Main Results:

  • The LCM exhibits symmetric and gradual resistive switching due to bidirectional Li-ion migration.
  • This linear weight update mechanism enhances ANN training efficiency.
  • Simulations show high accuracy in image classification with fewer training steps.

Conclusions:

  • The developed LCM is a promising candidate for energy-efficient neuromorphic circuits.
  • Its symmetric and linear switching behavior is key to improved ANN performance.
  • This memristor technology advances the field of hardware-based artificial intelligence.