Predicting optical properties of NiO films fabricated by RF magnetron sputtering: A machine learning approach
| dc.contributor.author | Yüksek, Ahmet Gürkan | |
| dc.contributor.author | Horoz, Sabit | |
| dc.contributor.author | Altuntaş, İsmail | |
| dc.contributor.author | Demi̇r, İlkay | |
| dc.contributor.author | Tüzemen, Ebru Ş. | |
| dc.date.accessioned | 2025-05-04T16:42:05Z | |
| dc.date.available | 2025-05-04T16:42:05Z | |
| dc.date.issued | 2025 | |
| dc.department | Sivas Cumhuriyet Üniversitesi | |
| dc.description.abstract | NiO films with different thicknesses (100, 150, 200, 250, 300 and 400 nm) were grown on glass substrates using the RF Magnetron sputtering method and their optical transmittance properties were analysed with a spectrophotometer. An innovative aspect of this work was the application of machine learning techniques used to derive new insights from experimental data. Four different machine learning algorithms -ANFIS, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Gaussian Process Regression (GPR)- were tested. While the models were trained using films of different thicknesses, a randomly selected 75 % of the whole dataset was used for model testing and the remaining 25 % of the films were used for testing the models. Among these, ANN and GPR models were found to be the most successful models. Using these models, the energy band gaps were estimated at 1 nm intervals and the values ranged from approximately 3.50 eV to 3.76 eV. © 2024 Elsevier GmbH | |
| dc.description.sponsorship | Sivas Cumhuriyet University Nanophotonic Application and Research Center | |
| dc.identifier.doi | 10.1016/j.ijleo.2024.172155 | |
| dc.identifier.issn | 0030-4026 | |
| dc.identifier.scopus | 2-s2.0-85212581921 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | https://doi.org/10.1016/j.ijleo.2024.172155 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12418/35061 | |
| dc.identifier.volume | 321 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Elsevier GmbH | |
| dc.relation.ispartof | Optik | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_Scopus_20250504 | |
| dc.subject | Energy band gap | |
| dc.subject | Machine Learning Applications | |
| dc.subject | NiO thin films | |
| dc.subject | Prediction | |
| dc.subject | Thickness | |
| dc.title | Predicting optical properties of NiO films fabricated by RF magnetron sputtering: A machine learning approach | |
| dc.type | Article |
