Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer
Abstract In this research, for some different Schottky type structures with and without a nanocomposite interfacial layer, the current–voltage (I–V) characteristics have been investigated by using different Machine Learning (ML) algorithms to predict and analyze the structures’ principal electric pa...
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-41000-z |
| _version_ | 1797560092226224128 |
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| author | Ali Barkhordari Hamid Reza Mashayekhi Pari Amiri Süleyman Özçelik Şemsettin Altındal Yashar Azizian-Kalandaragh |
| author_facet | Ali Barkhordari Hamid Reza Mashayekhi Pari Amiri Süleyman Özçelik Şemsettin Altındal Yashar Azizian-Kalandaragh |
| author_sort | Ali Barkhordari |
| collection | DOAJ |
| description | Abstract In this research, for some different Schottky type structures with and without a nanocomposite interfacial layer, the current–voltage (I–V) characteristics have been investigated by using different Machine Learning (ML) algorithms to predict and analyze the structures’ principal electric parameters such as leakage current (I0), barrier height ( $${\varphi }_{B0}$$ φ B 0 ), ideality factor (n), series resistance (Rs), shunt resistance (Rsh), rectifying ratio (RR), and interface states density (Nss). The interfacial nanocomposite layer is made by composing polyvinyl-pyrrolidone (PVP), zinc titanate (ZnTiO3), and graphene (Gr) nanostructures. The Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and Artificial Neural Network (ANN) are used as ML algorithms. The ML techniques training data are obtained using the thermionic emission method. Finally, by comparing the experimental and predicted results, the performance of the different ML algorithms in predicting the electrical parameters of Schottky diodes (SDs) has been compared to find the optimized ML algorithm. The ML predictions of basic electrical parameters by almost all algorithms are in good agreement with the actual values, while the SVR model has predicted closer values to the corresponding actual ones. The obtained results show that the quantity of the leakage current and Nss for MS type SD decreases, and φB0 increases with the interfacial layer usage, especially with graphene dopant. |
| first_indexed | 2024-03-10T17:55:39Z |
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| id | doaj.art-1ff287148b964e93b455609ce0fc633d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:55:39Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1ff287148b964e93b455609ce0fc633d2023-11-20T09:12:06ZengNature PortfolioScientific Reports2045-23222023-08-0113111810.1038/s41598-023-41000-zMachine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layerAli Barkhordari0Hamid Reza Mashayekhi1Pari Amiri2Süleyman Özçelik3Şemsettin Altındal4Yashar Azizian-Kalandaragh5Faculty of Physics, Shahid Bahonar University of KermanFaculty of Physics, Shahid Bahonar University of KermanDepartment of Engineering Sciences, University of Mohaghegh ArdabiliPhotonics Application and Research Center, Gazi UniversityDepartment of Physics, Faculty of Sciences, Gazi UniversityDepartment of Engineering Sciences, Faculty of Advanced Technologies, Sabalan University of Advanced Technologies (SUAT)Abstract In this research, for some different Schottky type structures with and without a nanocomposite interfacial layer, the current–voltage (I–V) characteristics have been investigated by using different Machine Learning (ML) algorithms to predict and analyze the structures’ principal electric parameters such as leakage current (I0), barrier height ( $${\varphi }_{B0}$$ φ B 0 ), ideality factor (n), series resistance (Rs), shunt resistance (Rsh), rectifying ratio (RR), and interface states density (Nss). The interfacial nanocomposite layer is made by composing polyvinyl-pyrrolidone (PVP), zinc titanate (ZnTiO3), and graphene (Gr) nanostructures. The Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and Artificial Neural Network (ANN) are used as ML algorithms. The ML techniques training data are obtained using the thermionic emission method. Finally, by comparing the experimental and predicted results, the performance of the different ML algorithms in predicting the electrical parameters of Schottky diodes (SDs) has been compared to find the optimized ML algorithm. The ML predictions of basic electrical parameters by almost all algorithms are in good agreement with the actual values, while the SVR model has predicted closer values to the corresponding actual ones. The obtained results show that the quantity of the leakage current and Nss for MS type SD decreases, and φB0 increases with the interfacial layer usage, especially with graphene dopant.https://doi.org/10.1038/s41598-023-41000-z |
| spellingShingle | Ali Barkhordari Hamid Reza Mashayekhi Pari Amiri Süleyman Özçelik Şemsettin Altındal Yashar Azizian-Kalandaragh Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer Scientific Reports |
| title | Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer |
| title_full | Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer |
| title_fullStr | Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer |
| title_full_unstemmed | Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer |
| title_short | Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer |
| title_sort | machine learning approach for predicting electrical features of schottky structures with graphene and zntio3 nanostructures doped in pvp interfacial layer |
| url | https://doi.org/10.1038/s41598-023-41000-z |
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