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

Biasing of FET01:22

Biasing of FET

988
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...
988
Field Effect Transistor01:29

Field Effect Transistor

1.7K
Field-effect transistors (FETs) are integral to electronic circuits and distinguished by their three-terminal setup: the gate, drain, and source. These transistors operate as unipolar devices, which utilize either electrons or holes as charge carriers, in contrast to bipolar transistors, which use both types of carriers. The primary function of the FET is to modulate the flow of these carriers from the source to the drain through a channel. The voltage difference between the gate and source...
1.7K
Semiconductors01:22

Semiconductors

1.8K
There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
1.8K
Biasing of Metal-Semiconductor Junctions01:27

Biasing of Metal-Semiconductor Junctions

896
Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...
896
MOSFET01:16

MOSFET

1.8K
The Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) plays a pivotal role in modern electronics thanks to its versatility and efficiency in controlling electrical currents. This device, also known as IGFET, MISFET, and MOSFET, has three main terminals: the Source, Drain, and Gate. MOSFETs are classified into n-channel or p-channel types based on the doping characteristics of their substrate and the source or drain regions.
In an n-MOSFET, the structure includes n-type source and drain...
1.8K
Metal-Semiconductor Junctions01:24

Metal-Semiconductor Junctions

1.4K
The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
Schottky Barriers
Schottky barriers arise when a metal with a work function (Φm) contacts a semiconductor with a different work function (Φs). Initially, electrons transfer until the Fermi levels of the metal and semiconductor align at equilibrium. For instance, if Φm > Φs, the semiconductor Fermi level is higher than the metal's before contact. The...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Hydrogen Peroxide-Enabled High-Quality Transition Interface for Top-Gated Molybdenum Disulfide Field-Effect Transistors.

ACS nano·2026
Same author

A machine learning perspective on three decades of methanol synthesis: research framework and experimental operation insights.

Communications engineering·2026
Same author

Designable van der Waals Crystal for Artificial Neuronal Cell Mimicking.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Fundamental and technical advances in bulk photovoltaics of 2D van der Waals materials.

Nanoscale horizons·2026
Same author

Hybrid ferroelectric-ionic memristive hardware for high scalability in-memory computing.

Nature communications·2026
Same author

Heterogeneous 3D Integration Based on Atomic-Level Electronics.

ACS applied materials & interfaces·2026

Related Experiment Video

Updated: Apr 22, 2026

A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy
10:40

A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy

Published on: April 8, 2018

7.6K

Towards Artificial Intelligence Hardware With 3D Integrated Ferroelectric Transistors.

Hyunho Seok1,2,3, Geonwook Kim4, Sihoon Son2,3

  • 1Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Small (Weinheim an Der Bergstrasse, Germany)
|April 21, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed a monolithic 3D integration platform using Indium Gallium Zinc Oxide (IGZO) and Hafnium Zirconium Oxide (HZO) ferroelectric field-effect transistors (FeFETs). This novel hardware accelerates artificial intelligence (AI) by enabling efficient compute-in-memory and neuromorphic computing.

Keywords:
convolutional neural networkferroelectricsmonolithic 3d integrationneuromorphic computingnon‐volatile memory

More Related Videos

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model
09:47

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Published on: October 18, 2015

9.3K
Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

2.5K

Related Experiment Videos

Last Updated: Apr 22, 2026

A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy
10:40

A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy

Published on: April 8, 2018

7.6K
Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model
09:47

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Published on: October 18, 2015

9.3K
Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

2.5K

Area of Science:

  • Materials Science and Engineering
  • Electrical Engineering
  • Computer Science (AI Hardware)

Background:

  • Modern AI workloads demand significant energy and bandwidth, stressing traditional von Neumann architectures due to data movement bottlenecks.
  • Compute-in-memory and neuromorphic systems offer potential solutions, but reliable 3D integration of analog synaptic devices is a key challenge.

Purpose of the Study:

  • To develop a monolithic 3D (M3D) integration platform for compact, energy-efficient neuromorphic hardware.
  • To demonstrate the feasibility of vertically stacking Indium Gallium Zinc Oxide (IGZO) access transistors and Hafnium Zirconium Oxide (HZO)-based ferroelectric transistors (FeFETs).

Main Methods:

  • Fabrication of two-tier and four-tier IGZO/FeFET architectures using a monolithic 3D integration platform.
  • Characterization of device structural integrity, elemental profiles, and HZO ferroelectricity across all tiers.
  • Evaluation of device switching reproducibility, retention (>10 years), endurance (>10^11 cycles), and multilevel conductance states.

Main Results:

  • Achieved excellent structural integrity and preserved ferroelectricity in the M3D-integrated IGZO/FeFETs.
  • Demonstrated stable synaptic device characteristics, including reproducible switching and long retention/endurance.
  • Attained high accuracy (95.0%-95.5%) for CIFAR-10 inference using mapped FeFETs in a CNN, approaching the software baseline (96.1%).
  • Successfully performed analog-domain convolution by encoding kernel weights into FeFET conductance states for edge-aware image processing.

Conclusions:

  • The M3D integration platform provides a scalable and reliable method for fabricating advanced neuromorphic hardware.
  • The demonstrated IGZO/FeFET devices are suitable for synaptic computing and next-generation compute-in-memory applications.
  • This technology paves the way for energy-efficient AI hardware, particularly for neuromorphic vision systems.