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

X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

3.8K
X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
3.8K

You might also read

Related Articles

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

Sort by
Same author

Correction to: Simultaneous prediction of early and delayed mortality in burn patients: a comparative machine learning analysis of feature importance in a single-center retrospective study.

BMC medical informatics and decision making·2026
Same author

Simultaneous prediction of early and delayed mortality in burn patients: a comparative machine learning analysis of feature importance in a single-center retrospective study.

BMC medical informatics and decision making·2025
Same author

Contrastive Forward-Forward: A training algorithm of vision transformer.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Author Correction: LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns.

Scientific reports·2025
Same author

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task.

Journal of neural engineering·2024
Same author

Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification.

Journal of computational neuroscience·2023

Related Experiment Video

Updated: Jun 10, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
11:14

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

13.8K

LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns.

Mehran Motamedi1, Reza Shidpour2, Mehdi Ezoji3

  • 1Department of Materials Engineering, Babol Noshirvani University of Technology, Babol 47148-71167, Iran.

Scientific Reports
|October 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning model using Long Short-Term Memory (LSTM) networks to predict semiconductor defect percentages from X-ray Diffraction (XRD) data. The method accurately forecasts defects in various materials, offering a novel approach for material science research.

Keywords:
Long short-term memoryNuance effectNumber of unitsSequence lengthSliding window technique

More Related Videos

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
07:24

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis

Published on: May 10, 2021

5.9K
Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

7.2K

Related Experiment Videos

Last Updated: Jun 10, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
11:14

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

13.8K
Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
07:24

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis

Published on: May 10, 2021

5.9K
Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

7.2K

Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computational Materials Science

Background:

  • Accurate prediction of point defect percentages is crucial for semiconductor performance.
  • Traditional methods for defect analysis can be time-consuming and complex.
  • Simulated X-ray Diffraction (XRD) data offers a potential avenue for non-destructive defect characterization.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting point defect percentages in semiconductor materials.
  • To leverage Long Short-Term Memory (LSTM) networks and sliding window techniques for enhanced feature extraction from simulated XRD data.
  • To assess the model's generalizability across different semiconductor materials and crystal structures.

Main Methods:

  • Utilized Long Short-Term Memory (LSTM) networks for time-series analysis of simulated XRD data.
  • Implemented a sliding window technique for effective feature extraction and capturing temporal dependencies.
  • Trained and optimized the model using silicon-simulated XRD data, exploring various sequence lengths and LSTM units.

Main Results:

  • The optimized LSTM model (3501 sequence length, 4500 units) achieved a low mean absolute error of 0.021.
  • The model successfully predicted defect percentages from 0-10% in silicon and other materials like AlAs, CdS, GaAs, Ge, and ZnS.
  • Observed a direct correlation between increasing defect percentages and rising background intensity in XRD patterns.
  • Identified consistent prediction trends for materials with Diamond Cubic and Zinc Blende crystal structures.

Conclusions:

  • The proposed LSTM-based approach provides an accurate and efficient method for predicting semiconductor point defect percentages from simulated XRD data.
  • The sliding window technique enhances the model's ability to generalize across diverse semiconductor materials.
  • This methodology offers a promising tool for materials characterization and quality control in semiconductor manufacturing.