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

330
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...
330
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

295
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
295
Field Effect Transistor01:29

Field Effect Transistor

490
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...
490
Small-Signal Analysis of MOSFET Amplifiers01:23

Small-Signal Analysis of MOSFET Amplifiers

625
In small-signal analysis, a MOSFET transistor amplifier acts as a linear amplifier when operating in its saturation region. The gate-to-source voltage (VGS) of the MOSFET is the sum of the DC biasing voltage and the small time-varying input signal. This combination sets up the operating point and modulates the drain current (ID) that flows from the drain to the source. When a small AC signal is superimposed on the DC bias voltage at the gate, the instantaneous drain current comprises three...
625
Leaky Scanning02:28

Leaky Scanning

5.2K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.2K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

263
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
263

You might also read

Related Articles

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

Sort by
Same author

Sub-mV tunable photonic p-bits for probabilistic computing.

Science advances·2026
Same author

Oscillatory Neural Network with High-Order Sub-Harmonic Injection Locking.

ACS applied materials & interfaces·2026
Same author

CMOS compatible probabilistic computing hardware with cointegrated reconfigurable p-bits and synapse arrays.

Nature communications·2026
Same author

Scalable Ising machine composed entirely of Si transistors.

Science advances·2026
Same author

Bioinspired Tripartite Synaptor with Conjoined Twin Transistors for Homeostasis in Neuromorphic Hardware.

ACS applied materials & interfaces·2026
Same author

Multi-State Probabilistic Computing Using Floating-Body MOSFETs Based on the Potts Model for Solving Complex Combinatorial Optimization Problems.

Advanced materials (Deerfield Beach, Fla.)·2026

Related Experiment Video

Updated: Jul 29, 2025

Sensitive Detection of Proteopathic Seeding Activity with FRET Flow Cytometry
12:31

Sensitive Detection of Proteopathic Seeding Activity with FRET Flow Cytometry

Published on: December 8, 2015

15.1K

Leaky FinFET for Reservoir Computing with Temporal Signal Processing.

Joon-Kyu Han1, Seong-Yun Yun1, Ji-Man Yu1

  • 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

ACS Applied Materials & Interfaces
|May 25, 2023
PubMed
Summary

This study demonstrates a novel leaky Fin-shaped field-effect transistor (L-FinFET) as a physical reservoir for efficient reservoir computing. This low-power, compact device enables effective temporal data processing and handwritten digit classification.

Keywords:
charge trapleaky fin-shaped field-effect transistor (L-FinFET)reservoir computingshort-term memorytemporal signal processing

More Related Videos

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

109
Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping
14:58

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

Published on: June 3, 2015

14.8K

Related Experiment Videos

Last Updated: Jul 29, 2025

Sensitive Detection of Proteopathic Seeding Activity with FRET Flow Cytometry
12:31

Sensitive Detection of Proteopathic Seeding Activity with FRET Flow Cytometry

Published on: December 8, 2015

15.1K
High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

109
Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping
14:58

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

Published on: June 3, 2015

14.8K

Area of Science:

  • Neuromorphic Engineering
  • Solid-State Electronics
  • Artificial Intelligence Hardware

Background:

  • Reservoir computing offers reduced costs for recurrent neural networks in temporal data processing.
  • Physical reservoirs are essential for hardware implementation, transforming sequential inputs into high-dimensional spaces.
  • Existing methods face challenges in power consumption and device footprint.

Purpose of the Study:

  • To demonstrate a physical reservoir using a leaky Fin-shaped field-effect transistor (L-FinFET).
  • To leverage the short-term memory property of L-FinFETs for temporal signal processing.
  • To showcase the potential of L-FinFETs for low-power, compact reservoir computing hardware.

Main Methods:

  • Fabrication and characterization of a leaky Fin-shaped field-effect transistor (L-FinFET) as a physical reservoir.
  • Utilizing the inherent short-term memory property arising from the absence of an energy barrier in the L-FinFET.
  • Experimental validation of 4-bit reservoir operations with 16 states for temporal signal processing.

Main Results:

  • The L-FinFET reservoir successfully implements short-term memory without losing multiple memory states.
  • Demonstrated very low power consumption during temporal input encoding due to the gate's insulation.
  • Achieved effective classification of handwritten digits from the Modified National Institute of Standards and Technology dataset using reservoir computing.

Conclusions:

  • The L-FinFET serves as a viable physical reservoir for efficient temporal data processing.
  • The device offers significant advantages in terms of low power consumption and reduced chip size.
  • This work paves the way for practical, hardware-based reservoir computing applications.