Crystals are a vital component of many machines. Familiar materials used in industry contain polycrystalline components, including metal alloys, ceramics and semiconductors.
As polycrystals are made up of many crystals, they have a complex microstructure and their properties vary greatly depending on how the crystal grains are orientated. This is especially important for the silicon crystals used in solar cells, smartphones and computers.
"To obtain a polycrystalline material that can be used effectively in industry, control and measurement of grain orientation distribution is required," said Professor Noritaka Usami, of Nagoya University.
"However, this is hindered by the expensive equipment and time current techniques need to measure large-area samples."
A Nagoya University team consisting of Professor Usami from the Graduate School of Engineering and Professor Hiroaki Kudo from the Graduate School of Informatics, in collaboration with RIKEN, have applied a machine-learning model that assesses photographs taken by illuminating the surface of a polycrystalline silicon material from various directions. They found that the AI successfully predicted the grain orientation distribution.
Image credit: Dr. Takuto Kojima
"The time required for this measurement was about 1.5 hours for taking optical photographs, training the machine-learning model, and predicting the orientation, which is much faster than conventional techniques, which take about 14 hours," Usami said. "It also enables measurement of large-area materials that were impossible with conventional methods."
Usami has high hopes for the use of the team's technique in industry. "This is a technology that will revolutionise materials development," Usami said. "This research is intended for all researchers and engineers who develop polycrystalline materials. It would be possible to manufacture an orientation analysis system of polycrystalline materials that packages an image data collection and a crystal orientation prediction model based on machine learning. We expect that many companies dealing with polycrystalline materials would install such equipment."
The results have been published in a research paper - 'A machine learning-based prediction of crystal orientations for multicrystalline materials' - in the journal APL Machine Learning.
