Drift-dIffusion transport-reaction modelling of Dopants and Ionic defecTs in semiconductor devices (DIDIT) (DIDIT)

Description

Mastering intrinsic and extrinsic doping of semiconductors is a key ingredient to the function and success of optoelectronic devices based on these materials. Analytical and numerical models describing the physical properties of these systems have enabled significant progress in the fields of organic semiconductors (OSCs) and of halide perovskites (HaP). Nevertheless, models offering a comprehensive description of the transport-reaction properties of dopants and ionic defects are currently missing. Today, this leaves a knowledge gap that is holding back the identification and understanding of fundamental processes underlying the complex electrical behaviour of devices based on doped polymer OSCs and mixed ionic-electronic conducting HaPs. Addressing this limitation for these two use-cases is the subject of the DIDIT project.

Specifically, the ability of modelling the drift and diffusion of dopants within OSCs is essential to study the effect of dopant redistribution during device operation, a key issue that often constrains the range of suitable dopant chemical structures. In the field of HaPs, modelling of the transport of ionic defects is well-established. Extending these models to include the redox activity of defects would directly address urgent open questions about the mechanisms underlying “anomalous” photovoltaic response and degradation of HaP based solar cells. Therefore, the fact that transport and reaction properties of dopants and ionic defects are not covered comprehensively and simultaneously in available numerical models is a major bottleneck for both fields of OSCs and HaPs.
Within DIDIT, a set of models addressing the comprehensive transport-reaction modelling of dopants and ionic defects in semiconductors will be developed. One of the models will address the doping mechanism in OSCs, including the explicit account of unreacted dopants, charge transfer complexes between dopants and OSCs and ionised dopant, and of the transport of each of these species. This will be integrated with the established drift-diffusion framework of electronic charge carriers.

Secondly, the project envisages the implementation of a transport-reaction model relevant to the electrochemistry of halide perovskite materials and devices. Starting from the development addressing OSC systems and focusing on the iodide defect chemistry, the model will be used to demonstrate the full ionic and electronic drift-diffusion simulation of the electrical response of HaP solar cells. The model will enable the identification of physical and chemical scenarios that can explain the complex current-voltage and impedance response evolution of these devices during operation.

Finally, the developed models will be validated with dedicated experiments that aim to track the time dependent transport-reaction problem in a series of selected doped OSCs and HaP based devices. Further investigation of the validity and limitations of the models will be carried out by comparing its results for selected model-cases, with an explicit charge dynamic simulation through the kinetic Monte Carlo approach. These computational and experimental activities aimed at validating the developed models will be pursued in collaboration with international research collaborators.

In summary, DIDIT leverages a multi-disciplinary approach to develop and validate models enabling improved understanding of key fundamental questions relevant to performance instability and degradation of organic and HaP optoelectronic devices. The planned work will provide novel analytical and numerical tools to formulate and test mechanistic interpretations to complex experimental data. By addressing key bottlenecks in the application of energy and optoelectronic materials, the project will also highlight promising mitigation strategies as well as novel opportunities in the design of devices where the transport-reaction properties of dopants and ionic defects enable novel functionalities.

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