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AI enables new route to improving perovskite solar cells

22 Nov 2023

Researchers from Karlsruhe, Helmholtz Imaging, and Cancer Research Center, Germany, use AI to optimize perovskite layers.

Researchers from the Karlsruhe Institute of Technology (KIT), Germany, as well as the Helmholtz Imaging platforms at the German Cancer Research Center (DKFZ) and Helmholtz AI have now found a way to predict the quality of the perovskite layers and thus solar cells using machine learning and new artificial methods.

The team describes its results from which improved production processes can be derived in Advanced Materials.

Perovskite tandem solar cells combine a perovskite with a conventional solar cell, for example based on silicon. These are widely considered to be “next-generation technology” because, with an efficiency of currently more than 33 percent, they are much more efficient than conventional silicon solar cells – with inexpensive starting materials and simple manufacturing methods.

The prerequisite for achieving this level of efficiency is a high-quality and thin perovskite layer. “One of the biggest challenges is to produce these high-quality so-called multicrystalline thin films using cost-effective and scalable processes without defects and holes,” commented tenure-track professor Ulrich W. Paetzold from the Institute of Microstructure Technology and the Light Engineering Institute at KIT.

Even under apparently perfect conditions in the laboratory, unknown influences can lead to fluctuations in the quality of the semiconductor layers: “This ultimately prevents the rapid start of industrial production of these highly efficient solar cells, which we so urgently need for the energy transition,” said Paetzold.

AI methods

In order to find out which factors influence the coating, an interdisciplinary team of perovskite solar cell experts from KIT teamed up with machine learning and explainable artificial intelligence specialists from Helmholtz Imaging and Helmholtz AI at KIT.

The researchers have developed AI methods that train and analyze neural networks using a large dataset. The dataset includes video recordings of the photoluminescence of the perovskite thin films during the manufacturing process.

“Since even experts couldn’t see anything remarkable on the thin films, the idea arose to train an AI for machine learning (deep learning) to find hidden indicators of a good or bad coating in the millions of data from the videos,” said Lukas Klein and Sebastian Ziegler from Helmholtz Imaging at the DKFZ.

In order to filter and analyze the very broad information provided by deep learning AI, the researchers subsequently used methods of explainable artificial intelligence.

Follow-up research

In the experiment, the researchers were able to see that the photoluminescence varies during production and this influences the coating quality. “What was crucial in our work was that we specifically used XAI methods to see which factors would have to change for a high-quality solar cell,” said Klein and Ziegler.

“That is usually not the case. Most of the time, XAI is only used as a kind of guardrail to avoid errors when building AI models: “This is a paradigm shift, and the fact that we can systematically gain highly relevant findings in materials science in this way is new.”

Because the answer is based on the variation of the photoluminescence enabled the researchers to go further. After appropriate training of the neural networks, the AI was able to predict whether the solar cell would achieve low or high efficiency, depending on when and what variation in light emission occurred during production.

“These are extremely exciting results,” said Ulrich Paetzold. “Thanks to the combined use of AI, we have an idea of which adjustments we need to make first in order to improve production. We can carry out our experiments in a more targeted manner and no longer have to search for a needle in a haystack in the dark. This is a blueprint for follow-up research, including for many other aspects in energy research and materials science.”

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