Defect Engineering, Advanced Modelling and Characterization for Next Generation Opto-Electronic-Ionic Devices (OptEIon)

Description

In the project Defect Engineering, Advanced Modelling and Characterization for Next Generation Opto-Electronic-Ionic Devices (OptEIon), we investigate the interplay between electron and ionic conductivity in metal-halide perovskite semiconductors. We plan to prepare samples, characterize the transient optoelectronic response, and employ device modelling to unravel the effects of electronic and ionic defects. Finally, we aim to exploit the mixed ionic-electronic conductivity in novel devices.

Perovskite solar cells have attracted grain interest because of the high efficiencies obtained (>25%) and facile preparation methods. Key to these impressive results is the high tolerance of the perovskite semiconductor versus defects. On the other hand, perovskite solar cells and LEDs suffer from instabilities, partially due to mobile ionic charges. These ions originate from crystal defects (e.g. vacancies as shown on the top left in the figure). Beyond being a stability concern, mobile ions influence the optoelectronic response of the device, e.g. by drift when a voltage is applied. This causes a belayed response that is visible, e.g. in a hysteresis when recording the current-voltage characteristics or in unconventional features in the impedance response.

Within this project, we intend to further characterize the interplay between ionic and electronic conductivity. We plan to fabricate perovskite films, characterize their response to voltage and illumination stimuli and use device simulation to extract physical parameters. To gain insights into nanoscopic processes, we plan to use tipenhanced spectroscopy techniques. On the simulation side, we want to evaluate, whether and to which extent physics-based modeling can be complemented by approaches based on machine learning.

The goal of this project is to control the effect of mobile ions and exploit them as modulator of loss processes in solar cells or in novel devices. A potential candidate are memristive devices. In such devices the conductivity depends on the history of the device, for instance on how much current was passed through it in the time before. Such devices might become interesting for future computing approaches such as neuromorphic computing.