Comparative study of various methods for extraction of multi- quantum wells Schottky diode parameters

Elyes Garoudja; Walid Filali; Slimane Oussalah; Noureddine Sengouga; Mohamed Henini

doi:10.1088/1674-4926/41/10/102401

Journals >Journal of Semiconductors >Volume 41 >Issue 10 >Page 102401 > Article

Journal of Semiconductors
Vol. 41, Issue 10, 102401 (2020)

Comparative study of various methods for extraction of multi- quantum wells Schottky diode parameters

Elyes Garoudja¹, Walid Filali¹, Slimane Oussalah², Noureddine Sengouga³, and Mohamed Henini⁴

Author Affiliations

¹Plateforme Technologique de Microfabrication, Centre de Développement des Technologies Avancées, cité 20 août 1956, Baba Hassen, Algiers, Algeria

²Microelectronics and Nanotechnology Division, Centre de Développement des Technologies Avancées, cité 20 août 1956, Baba Hassen, Algiers, Algeria

³Laboratory of Metallic and Semiconducting Materials, Université de Biskra, B.P 455, 07000 Biskra RP, Algeria

⁴School of Physics and Astronomy, Nottingham Nanotechnology and Nanoscience Center, University of Nottingham, Nottingham, NG7 2RD, UK

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DOI: 10.1088/1674-4926/41/10/102401 Cite this Article

Elyes Garoudja, Walid Filali, Slimane Oussalah, Noureddine Sengouga, Mohamed Henini. Comparative study of various methods for extraction of multi- quantum wells Schottky diode parameters[J]. Journal of Semiconductors, 2020, 41(10): 102401 Copy Citation Text

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Abstract

In this work, forward current voltage characteristics for multi-quantum wells Al_0.33Ga_0.67As Schottky diode were measured at temperature ranges from 100 to 300 K. The main parameters of this Schottky diode, such as the ideality factor, barrier height, series resistance and saturation current, have been extracted using both analytical and heuristics methods. Differential evolution (DE), particle swarm optimization (PSO) and artificial bee colony (ABC) have been chosen as candidate heuristics algorithms, while Cheung technic was selected as analytical extraction method. The obtained results show clearly the high performance of DE algorithms in terms of parameters accuracy, convergence speed and robustness.

${{I =\; }}{{{I}}_{\rm{s}}}\left\{ {{\rm{exp}}\left[ {\frac{{{{q}}\left( {{{V}} - {{I}}{{{R}}_{\rm{s}}}} \right)}}{{{{nKT}}}}} \right]- 1} \right\}.$

(1)

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${{{I}}_{\rm{s}}} = AA^*{{{T}}^{\rm{2}}}{\rm{exp}}\left( {\frac{{- q{{\rm{\phi}}_{\rm{b}}}}}{{{{KT}}}}} \right).$

(2)

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${\rm{RMSE}} = \sqrt {\frac{{{I}}}{{{L}}}\sum\limits_{i = 1}^{{L}} {{{\left[ {{f_{\rm{i}}}\left( {{{{I}}_{{\rm{meas}}}},{{{V}}_{{\rm{meas}}}}, \eta } \right)} \right]}^2}} } ,$

(3)

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$\begin{array}{l} \!\!\!\!\!\! {{f}}({{{I}}_{{\rm{meas}}}},{{{V}}_{{\rm{meas}}}},\eta) = {{{I}}_{{\rm{meas}}}}\; -\\ \qquad\qquad\;\;\;\;\;\;\quad \left( {{{{I}}_{\rm{s}}}\left\{ {{\rm{exp}}\left[ {\dfrac{{{{q}}\left( {{{{V}}_{{\rm{meas}}}} - {{{I}}_{{\rm{meas}}}}{{{R}}_{\rm{s}}}} \right)}}{{{{nkT}}}}} \right] - 1} \right\}} \right). \end{array}$

(4)