| Revista: | Journal of applied research and technology |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000427854 |
| ISSN: | 1665-6423 |
| Autores: | Attia, Attia A1 El Bana, Mohammed S1 Habashy, Doaa M1 Fouad, Suzan S1 El Bakry, Mahmoud Y1 |
| Instituciones: | 1Ain Shams University, Faculty of Education, El Cairo. Egipto |
| Año: | 2017 |
| Periodo: | Oct |
| Volumen: | 15 |
| Número: | 5 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | This paper uses an artificial neural network (ANN) and resilient back-propagation (Rprop) training algorithm to determine the optical constants of As30Se 70−x Sn x (0 ≤ x ≤ 3) thin films. The simulated values of the ANN are in good agreement with the experimental data. The ANN models performance was also examined to predict the simulated values for As30Se67Sn3 which was not included in the training and was found to be in accordance with the experimental data. The high precision of the ANN models as well as a great guessing performance have been exhibited. Moreover, the energy gap E g of As30Se 70−x Sn x (0 ≤ x ≤ 9) thin films were calculated theoretically |
| Disciplinas: | Ingeniería |
| Palabras clave: | Ingeniería de materiales, Películas delgadas, Arsénico-Selenio-Estaño, Semiconductores, Constantes ópticas, Brecha de energía |
| Keyword: | Materials engineering, Thin films, Arsenic-Selenium-Tin, Semiconductors, Optical constants, Energy gap |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
