Performance Enhancement of AlInGaN Quantum Well based UV-LED

Samadrita Das, T. R. Lenka, F. A. Talukdar, R. T. Velpula, Barsha Jain, H. P.T. Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, a light emitting diode in the ultra-violet range (UV-LED) with quantum well of AlInGaN is designed and analyzed through technology computer-aided design (TCAD) simulations. A thorough study is performed to find out the output optical characteristics of the LED. During the experiment we have varied the thickness of the well and aluminum concentration in the electron blocking layer in order to realize its impact on the device performance. The structure of the device and the characteristics of epitaxial layers play a very noteworthy role in the device's performance. The yield characteristics of the device depend on its structural layer. Because of this, various properties are optimized in order to improve the device's final performance. The LED device has gained importance over the past few decades. These are available for almost all ranges of wavelength right from deep ultra-violet to infra-red light region. The most efficient UV-LEDs are fabricated using AlInGaN with different atom proportions. The emission wavelength is correlated with the band gap of materials for such optoelectronic devices which can be varied from UV to IR.

Original languageEnglish (US)
Title of host publicationProceedings of the 2021 IEEE 18th India Council International Conference, INDICON 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665441759
DOIs
StatePublished - 2021
Event18th IEEE India Council International Conference, INDICON 2021 - Guwahati, India
Duration: Dec 19 2021Dec 21 2021

Publication series

NameProceedings of the 2021 IEEE 18th India Council International Conference, INDICON 2021

Conference

Conference18th IEEE India Council International Conference, INDICON 2021
Country/TerritoryIndia
CityGuwahati
Period12/19/2112/21/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

Keywords

  • AlInGaN
  • EBL
  • III-nitride
  • Quantum Well
  • UV-LED

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