Summary
Defects in semiconductor materials commonly deteriorate the performance of optoelectronic devices such as solar cells and light-emitting diodes. In the recently emerged and highly successful hybrid metal halide perovskite, some lattice defects are even mobile leading to mixed ionic-electronic conductivity. This and other outstanding properties (tunable bandgap, lower dimensional embodiments, solution processability) make the perovskite a very interesting material for research and application. At the same time, it suffers from various degradation processes, linked to these poorly understood ionic defects. The major questions are: Where and what are these defects? How are they formed and how can we control their movement?
OptEIon will provide answers to these questions.
Based on my expertise in the device physics and experience in perovskites I will proceed as follows: First, I will characterize the transient response of devices with different perovskite materials, different stoichiometry, partial pressure of constituents, temperature, etc. to find clear evidence for the nature of the mobile defects and their diffusion constant. Second, I will employ nano-scale characterization on cross sections of working devices to measure location and time evolution of defects causing recombination losses in solar cells. In addition to established measurement techniques, I will use tip-enhanced (near field) spectroscopic techniques, which can provide super-resolution imaging. Third, I will apply device simulation to examine the measurement results. I will furthermore evaluate how machine learning in combination with our physical model could be implemented to help analyse device data. Fourth, I will exploit the results by fabricating demonstrator memristor arrays that can be controlled by light.
The outcome will be more efficient and stable solar cells and novel optoelectronic devices such as memristors, which are supposed to herald a new era of neuromorphic computing.
OptEIon will provide answers to these questions.
Based on my expertise in the device physics and experience in perovskites I will proceed as follows: First, I will characterize the transient response of devices with different perovskite materials, different stoichiometry, partial pressure of constituents, temperature, etc. to find clear evidence for the nature of the mobile defects and their diffusion constant. Second, I will employ nano-scale characterization on cross sections of working devices to measure location and time evolution of defects causing recombination losses in solar cells. In addition to established measurement techniques, I will use tip-enhanced (near field) spectroscopic techniques, which can provide super-resolution imaging. Third, I will apply device simulation to examine the measurement results. I will furthermore evaluate how machine learning in combination with our physical model could be implemented to help analyse device data. Fourth, I will exploit the results by fabricating demonstrator memristor arrays that can be controlled by light.
The outcome will be more efficient and stable solar cells and novel optoelectronic devices such as memristors, which are supposed to herald a new era of neuromorphic computing.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/851676 |
| Start date: | 01-06-2020 |
| End date: | 31-05-2025 |
| Total budget - Public funding: | 1 980 735,00 Euro - 1 980 735,00 Euro |
Cordis data
Original description
Defects in semiconductor materials commonly deteriorate the performance of optoelectronic devices such as solar cells and light-emitting diodes. In the recently emerged and highly successful hybrid metal halide perovskite, some lattice defects are even mobile leading to mixed ionic-electronic conductivity. This and other outstanding properties (tunable bandgap, lower dimensional embodiments, solution processability) make the perovskite a very interesting material for research and application. At the same time, it suffers from various degradation processes, linked to these poorly understood ionic defects. The major questions are: Where and what are these defects? How are they formed and how can we control their movement?OptEIon will provide answers to these questions.
Based on my expertise in the device physics and experience in perovskites I will proceed as follows: First, I will characterize the transient response of devices with different perovskite materials, different stoichiometry, partial pressure of constituents, temperature, etc. to find clear evidence for the nature of the mobile defects and their diffusion constant. Second, I will employ nano-scale characterization on cross sections of working devices to measure location and time evolution of defects causing recombination losses in solar cells. In addition to established measurement techniques, I will use tip-enhanced (near field) spectroscopic techniques, which can provide super-resolution imaging. Third, I will apply device simulation to examine the measurement results. I will furthermore evaluate how machine learning in combination with our physical model could be implemented to help analyse device data. Fourth, I will exploit the results by fabricating demonstrator memristor arrays that can be controlled by light.
The outcome will be more efficient and stable solar cells and novel optoelectronic devices such as memristors, which are supposed to herald a new era of neuromorphic computing.
Status
SIGNEDCall topic
ERC-2019-STGUpdate Date
27-04-2024
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