Monthly Archives: May 2020

S-shaped IV-curves in organic semiconductors

Organic electronics, Self-heating
Simulated Joule heat density [W/cm^3] in an organic thin-film transistor

Simulated current–voltage characteristics using the electrothermal drift–diffusion model for different reference mobilities

Organic semiconductors show a complex interplay between charge-carrier and heat flow. In particular, due to Joule self-heating the temperature in an organic device increases, which in turn leads to an increase in conductivity due to temperature activated hopping transport. A positive feedback loop arises. In recent publications by our colleagues from the Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), S-shaped current-voltage characteristics of organic devices have been observed in experiments. In previous work, we have modeled this interplay via a coarse thermistor model for the net current and heat flow (see here).

In order to give a more detailed description of the processes, we extended our drift-diffusion simulation tool ddfermi to take self-heating and the positive feedback in the mobility laws, that are usually used for organic materials, into account. The details can be found in a recently published paper with out partners from the IAPP and the company m4sim GmbH in the Journal of Computational Electronics.

In the paper, an electrothermal drift–diffusion model for organic semiconductor devices with Gauss–Fermi statistics and positive temperature feedback for the charge carrier mobilities is introduced. We apply temperature-dependent Ohmic contact boundary conditions for the electrostatic potential and discretize the system by a finite volume based generalized Scharfetter–Gummel scheme. Using path-following techniques, we demonstrate that the model exhibits S-shaped current–voltage curves with regions of negative differential resistance.

Drift–diffusion simulation of S-shaped current–voltage relations for organic semiconductor devices
Duy Hai Doan, Axel Fischer, Jürgen Fuhrmann, Annegret Glitzky & Matthias Liero
Journal of Computational Electronics (2020)
https://doi.org/10.1007/s10825-020-01505-6

Open Access funding provided by Projekt DEAL. The work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—The Berlin Mathematics Research Center MATH+ (EXC-2046/1, Project ID: 390685689) in transition project SE18, Project AA2-1 and AA2-6 and the DFG Project EFOD (Grant No. RE 3198/6-1).

Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes

Quantum dots, ,

Semiconductor quantum dots (QDs) are of interest in many application areas due to their electronic properties. Transmission electron microscopy (TEM) images can be used to examine the QD geometry, distribution and strain profile, which will be helpful in the fabrication of QDs with specific electronic properties.

In order to link the contrasts in TEM images with shapes and concentration of these QDs it is crucial to combine strain calculations with TEM image simulations. In collaboration with researchers from TU Berlin, we recently presented a mathematical model and a tool chain for the numerical simulation of TEM images for semiconductor QDs, published in Optical and Quantum Electronics (Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes). We simulated lens-shaped and pyramidal indium gallium arsenide QDs embedded in a gallium arsenide matrix and compared the resulting TEM images to experimental ones. This tool chain will be applied to generate a database of simulated TEM images, which is a key element of a novel concept for model-based geometry reconstruction of semiconductor QDs, involving machine learning techniques.

This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—MATH+ (Project EF3-1) and CRC 787 “Semiconductor Nanophotonics” under Project A4.