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Related Concept Videos

Photoluminescence: Fluorescence and Phosphorescence01:23

Photoluminescence: Fluorescence and Phosphorescence

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Photoluminescence is a process where a molecule absorbs light energy and re-emits it in the form of light. This phenomenon occurs when a substance absorbs photons, promoting its electrons to higher energy level excited states, followed by a relaxation process in which the electrons return to their original ground state energy levels and emit light. Photoluminescence is widely observed in various materials, including semiconductors, and organic and inorganic compounds.
A pair of electrons in a...
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Semiconductors01:22

Semiconductors

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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...
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Types of Semiconductors01:20

Types of Semiconductors

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Intrinsic semiconductors are highly pure materials with no impurities. At absolute zero, these semiconductors behave as perfect insulators because all the valence electrons are bound, and the conduction band is empty, disallowing electrical conduction. The Fermi level is a concept used to describe the probability of occupancy of energy levels by electrons at thermal equilibrium. In intrinsic semiconductors, the Fermi level is positioned at the midpoint of the energy gap at absolute zero. When...
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Photoluminescence: Applications01:14

Photoluminescence: Applications

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Photoluminescence offers a wide range of applications due to its inherent sensitivity and selectivity. This technique allows for both direct and indirect analyses of the analyte. Direct quantitative analysis is possible when the analyte exhibits a favorable quantum yield for fluorescence or phosphorescence. However, an indirect analysis may be feasible if the analyte is not fluorescent or phosphorescent, or if the quantum yield is unfavorable. Indirect methods include reacting the analyte with...
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Fluorescence and Phosphorescence: Instrumentation01:25

Fluorescence and Phosphorescence: Instrumentation

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Fluorometers and spectrofluorometers are two types of instruments used for measuring molecular fluorescence. These instruments differ in how they select excitation and emission wavelengths and the type of light sources they utilize. Fluorometers use absorption interference filters to choose excitation and emission wavelengths. The excitation source in a fluorometer is typically a low-pressure mercury vapor lamp that emits intense lines distributed throughout the ultraviolet and visible regions.
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UV–Vis Spectroscopy: Molecular Electronic Transitions01:16

UV–Vis Spectroscopy: Molecular Electronic Transitions

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In Ultraviolet–Visible (UV–Vis) spectroscopy, the absorption of electromagnetic radiation is used to probe the electronic structure of molecules. This technique provides insights into molecular electronic transitions, particularly the movement of electrons between different molecular orbitals. Radiation is absorbed if the energy of the electromagnetic radiation passing through the molecule is precisely equal to the energy difference between the excited and ground states. During this...
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Classification of Semiconductors Using Photoluminescence Spectroscopy and Machine Learning.

Yinchuan Yu1, Matthew D McCluskey1

  • 1Department of Physics and Astronomy, Washington State University, Pullman, WA, USA.

Applied Spectroscopy
|August 3, 2021
PubMed
Summary

Machine learning accurately identifies semiconductor materials from photoluminescence spectra. This method uses neural networks to classify six common semiconductors with over 90% accuracy.

Keywords:
Photoluminescencefluorescencemachine learningneural network

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Area of Science:

  • Materials Science
  • Spectroscopy
  • Artificial Intelligence

Background:

  • Photoluminescence spectroscopy is a key technique for semiconductor characterization.
  • It relies on the optical emission of light after photon absorption.
  • Accurate material identification is crucial for semiconductor applications.

Purpose of the Study:

  • To develop a machine learning-based method for identifying semiconductor substances from their photoluminescence spectra.
  • To classify six specific semiconductor materials: gallium oxide (Ga2O3), zinc oxide (ZnO), gallium nitride (GaN), cadmium sulfide (CdS), tungsten disulfide (WS2), and cesium lead bromide (CsPbBr3).

Main Methods:

  • Utilized simulated photoluminescence spectra as input data for neural network models.
  • Trained neural networks to map spectral data to specific semiconductor identities.
  • Evaluated the classification accuracy of the developed algorithm.

Main Results:

  • The machine learning algorithm achieved high accuracy (>90%) in identifying the six chosen semiconductor materials.
  • The model successfully identified a mixed sample containing gallium oxide and zinc oxide.
  • Demonstrated the efficacy of photoluminescence spectroscopy combined with machine learning for material identification.

Conclusions:

  • Machine learning, specifically neural networks, offers an efficient and accurate approach to identifying semiconductor materials using photoluminescence spectra.
  • This technique holds promise for automated material characterization and quality control in semiconductor research and industry.