Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO3 nanostructures doped in PVP interfacial layer

Abstract

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 (I 0 ), barrier height ( $${\varphi }_{B0}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>φ</mml:mi> <mml:mrow> <mml:mi>B</mml:mi> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> </mml:math> ), ideality factor (n), series resistance (R s ), shunt resistance (R sh ), rectifying ratio (RR), and interface states density (N ss ). The interfacial nanocomposite layer is made by composing polyvinyl-pyrrolidone (PVP), zinc titanate (ZnTiO 3 ), 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 N ss for MS type SD decreases, and φ B0 increases with the interfacial layer usage, especially with graphene dopant.