Authors: Noritaka Usami
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Materials Chemistry

(Invited) Application of Machine Learning for High-Performance Multicrystalline Materials Many practical materials are multicrystalline to contain complex microstructures and crystal defects. Owing to its complexity, universal guideline regarding ideal microstructures to maximize macroscopic performance has not been fully established. Therefore, we attempt to pioneer “multicrystalline informatics” through collaboration of experiments, theory, computation, and machine learning to show how we can obtain high-performance multicrystalline materials. We employ silicon as a model material, and prepare various useful machine learning tools for efficient materials development. One example is to predict distribution of crystal orientations in a large sample (e.g. whole 6-inch wafer) from optical images. We started with surface treatment of multicrystalline silicon wafers so that the reflection become sensitive to the crystal orientations. Multiple optical images were captured from each wafer with white-light illumination from different orientations. In addition, orientations of each crystal grain for a couple of wafers were measured by X-ray Laue scanner. The obtained data sets of reflection patterns and crystal orientations were used for training, validation, and test of a long short-term memory recurrent neural network. The trained model was applied to a test wafer to contain ~1,000 crystal grains, and the median of the estimated errors was ~3 degree. Another example is to predict probability of generation of dislocation clusters. For this purpose, a large quantity of photoluminescence (PL) images was collected from wafers obtained from a same ingot. Three-dimensional reconstruction of PL images after extracting dislocation clusters by image processing successfully visualized distribution of dislocation clusters. Therefore, we can collect many small PL images as positive samples to act as the source of dislocations as well as negative samples. The obtained data sets were used for transfer learning of pre-trained convolutional neural network for image classification. The final output layer was changed to contain two outputs if the input image acts as the source of dislocations or not, and weights for the last edges were determined using the data sets. The network was applied to test wafers so that we can visualize probability distribution of generation of dislocation clusters. A part of sources was used for multiscale structural characterizations to clarify microscopic origins. In addition, we are working on various machine learning models and statistical analysis for multicrystalline materials. Integration of such models is believed to be beneficial to show how we can maximize macroscopic performance of multicrystalline materials. This work was supported by JST/CREST, Grant No. JPMJCR17J1 (2017-2023). Wafer ; Materials science ; Dislocation ; Crystal (programming language) ; Ingot ; Artificial intelligence ; Silicon ; Microstructure ; Reflection (computer programming) ; Machine learning ; Computer science ; Optoelectronics ; Composite material Machine Learning in Materials Science