Electrical and Photodetector Characteristics of Schottky Structures Interlaid with P(EHA) and P(EHA--AA) Functional Polymers by the iCVD Method.

In this study, poly(2-ethylhexyl acrylate) (PEHA) homopolymer and its copolymer combined with acrylic acid P(EHA--AA) were employed as interfaces in two separate Schottky structures. First, both interfaces were grown by initiated chemical vapor deposition (iCVD), which provides much better deposition control and homogeneous coating compared to solution-phase methods. In addition to this advantageous method, the effects of two different polymers, one of which is better able to adhere to the crystal surface on which it is formed than the other, on the optoelectronic properties have been studied.

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

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), barrier height ([Formula: see text]), ideality factor (n), series resistance (R), shunt resistance (R), rectifying ratio (RR), and interface states density (N). The interfacial nanocomposite layer is made by composing polyvinyl-pyrrolidone (PVP), zinc titanate (ZnTiO), 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.