Optimum Experimental Design for EGDM Modeled Organic Semiconductor Devices

TL;DR

This paper applies optimal experimental design to improve parameter estimation in organic semiconductor devices modeled by EGDM, using advanced numerical methods to optimize experiments and reduce uncertainty.

Contribution

It introduces an extended Gummel method and uses automatic differentiation to enhance OED for EGDM-modeled organic semiconductors, leading to more precise parameter estimation.

Findings

OED significantly reduces confidence regions of parameters.

New experimental setups improve parameter accuracy.

Method enhances modeling precision for organic semiconductors.

Abstract

We apply optimum experimental design (OED) to organic semiconductors modeled by the extended Gaussian disorder model (EGDM) which was developed by Pasveer et al. We present an extended Gummel method to decouple the corresponding system of equations and use automatic differentiation to get derivatives with the required accuracy for OED. We show in two examples, whose parameters are taken from Pasveer et al. and Mensfoort and Coehoorn that the linearized confidence regions of the parameters can be reduced significantly by applying OED resulting in new experiments with a different setup.