Numerical simulation of charge transport in disordered organic semiconductor devices

Abstract

For the design of organic semiconductor devices such as organic light-emitting devices and solar cells, it is of crucial importance to solve the underlying charge transport equations efficiently and accurately. Only a fast and robust solver allows the use of fitting algorithms for parameter extraction and variation. Introducing appropriate models for organic semiconductors that account for the disordered nature of hopping transport leads to increasingly nonlinear and more strongly coupled equations. The solution procedures we present in this study offer a versatile, robust, and efficient means of simulating organic semiconductor devices. They allow for the direct solution of the steady-state drift-diffusion problem. We demonstrate that the numerical methods perform well in combination with advanced physical transport models such as energetic Gaussian disorder, density-dependent and field-dependent mobilities, the generalized Einstein diffusion, traps, and its consistent charge injection model.

Insights into the Failure Mechanisms of Organic Photodetectors

“…This methodology is summarized in Figure S4, the basic equations are given in Supplementary Information (SI). Simmons et al [23] [24] have extended the theory developed by Haering and Adams, [25] and proposed to apply a high electric field during the rising of temperature in order to avoid recombination and re-trapping. For OPDs, an internal electric field in the 10 4 -10 5 V cm -1 range is present at the short circuit conditions due to the difference of electrode work functions.…”

Effect of Coulomb correlation on charge transport in disordered organic semiconductors

“…Assuming incoherent hopping of charge carriers between localized states and using advanced three-dimensional (3D) mechanistic modeling techniques, it is now possible to predict the macroscopic charge transport properties of organic semiconductors using microscopic information at the molecular level, including a Gaussian distribution of site energies, the spatial packing of the material, the distribution of charge transfer integrals, and the reorganization energies [5][6][7][8][9][10][11][12]. Device modeling has been carried out using drift-diffusion [13][14][15][16][17][18], master-equation (ME) [19][20][21][22][23], and kinetic Monte Carlo (KMC) [2,[24][25][26][27][28] simulation methods. Within driftdiffusion and ME device simulations, the Coulomb interaction between the charge carriers is treated using a mean-field approach: using the Poisson equation, the (time-averaged) charge density is used to calculate the (time-averaged) electric field due to the space charge in the device.…”

Numerical analysis of exciton dynamics in organic light-emitting devices and solar cells

“…At metalorganic interfaces, the charge transport level is affected by the applied field and by the image charge potential, lowering therefore the injection energy barrier. 5 This effect, already known for conventional models, is even stronger when the Gaussian energy broadening of the molecular orbitals is accounted for. In fact, the more broad the HOMO or LUMO, the easier the injection from the electrodes.…”