Optimal Identification of Be-Doped Al0.29Ga0.71As Schottky Diode Parameters Using Dragonfly Algorithm: A Thermal Effect Study

Filali, Walid; Amrani, Rachid; Garoudja, Elyes; Oussalah, Slimane; Lekoui, Fouaz; Oukerimi, Zineb; Sengouga, Nouredine; Henini, Mohamed

doi:10.1016/j.spmi.2021.107085

Optimal Identification of Be-Doped Al0.29Ga0.71As Schottky Diode Parameters Using Dragonfly Algorithm: A Thermal Effect Study

Filali, Walid; Amrani, Rachid; Garoudja, Elyes; Oussalah, Slimane; Lekoui, Fouaz; Oukerimi, Zineb; Sengouga, Nouredine; Henini, Mohamed

Authors

Walid Filali

Rachid Amrani

Elyes Garoudja

Slimane Oussalah

Fouaz Lekoui

Zineb Oukerimi

Nouredine Sengouga

Professor MOHAMED HENINI MOHAMED.HENINI@NOTTINGHAM.AC.UK
PROFESSOR OF APPLIED PHYSICS

Abstract

In this work, a recent heuristic method called Dragonfly Algorithm (DA) has been employed for the first time to investigate the temperature effect on the Schottky diode electrical parameters. Beryllium-doped Al0.29Ga0.71As Schottky diodes grown by molecular beam epitaxy (MBE) have been used to validate the suggested method. The proposed approach is based on the analysis of current-voltage-temperature (I–V-T) and capacitance-voltage (C–V) characteristics. Furthermore, the interface state density (Nss) as function of the difference between the surface state energy and valence band energy (Ess – Ev) was determined. The obtained results demonstrate the high efficiency of this strategy to accurately determine the electrical parameters and investigate their temperature dependency. This efficiency can be clearly remarked from the well fit between both predicted and measured current characteristics.

Citation

Filali, W., Amrani, R., Garoudja, E., Oussalah, S., Lekoui, F., Oukerimi, Z., Sengouga, N., & Henini, M. (2021). Optimal Identification of Be-Doped Al0.29Ga0.71As Schottky Diode Parameters Using Dragonfly Algorithm: A Thermal Effect Study. Superlattices and Microstructures, 160, Article 107085. https://doi.org/10.1016/j.spmi.2021.107085

Journal Article Type	Article
Acceptance Date	Oct 31, 2021
Online Publication Date	Nov 3, 2021
Publication Date	2021-12
Deposit Date	Nov 4, 2021
Publicly Available Date	Nov 4, 2022
Journal	Superlattices and Microstructures
Print ISSN	0749-6036
Electronic ISSN	1096-3677
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	160
Article Number	107085
DOI	https://doi.org/10.1016/j.spmi.2021.107085
Public URL	https://nottingham-repository.worktribe.com/output/6611105
Publisher URL	https://www.sciencedirect.com/science/article/abs/pii/S0749603621002834