Machine learning models for efficient characterization of Schottky barrier photodiode internal parameters.

We propose ANN-based models to analyze and extract the internal parameters of a Schottky photodiode (SPD) without presenting them with any knowledge of the highly nonlinear thermionic emission (TE) expression of the device current. We train, evaluate and demonstrate the ML models on thirty-six private datasets from three previously published devices, which denote current responses under illumination and ambient temperature of graphene oxide (GO) doped p-Si Schottky barrier diodes (SBDs). The GO doping levels are 0%, 1%, 3%, 5%, and 10%.

Bias and illumination-dependent room temperature negative differential conductance in Ni-doped ZnO/p-Si Schottky photodiodes for quantum optics applications.

In this article, evidence for the existence of illumination and bias-dependent negative differential conductance (NDC) in Ni-doped Al/ZnO/p-Si Schottky diodes, and the possible mechanism for its origin, are presented. The atomic percentages of Ni doping were 0%, 3%, 5%, and 10%. NDC is observed between -1.