Predicting optical properties of NiO films fabricated by RF magnetron sputtering: A machine learning approach

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Atıf İçin Kopyala

YÜKSEK A. G., Horoz S., ALTUNTAŞ İ., DEMİR İ., Tüzemen E.

Optik, cilt.321, 2025 (SCI-Expanded)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 321
• Basım Tarihi: 2025
• Doi Numarası: 10.1016/j.ijleo.2024.172155
• Dergi Adı: Optik
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC
• Anahtar Kelimeler: Energy band gap, Machine Learning Applications, NiO thin films, Prediction, Thickness
• Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

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.