Machine learning application for prediction of sapphire crystals defects
| Autor: | Ravi Kumar, Maxim Anikeev, Yulia Klunnikova, Alexey Vladimirovich Filimonov |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Materials science
Artificial neural network Computer Networks and Communications Generalization business.industry 020209 energy Crucible Crystal growth 02 engineering and technology 021001 nanoscience & nanotechnology Machine learning computer.software_genre Crystal Condensed Matter::Superconductivity Signal Processing Thermal 0202 electrical engineering electronic engineering information engineering Sapphire Node (circuits) Artificial intelligence Electrical and Electronic Engineering 0210 nano-technology business computer |
| Zdroj: | Journal of Electronic Science and Technology. 18:100029 |
| ISSN: | 1674-862X |
| DOI: | 10.1016/j.jnlest.2020.100029 |
| Popis: | We investigate the impact of different numbers of positive and negative examples on machine learning for sapphire crystals defects prediction. We obtain the models of crystal growth parameters influence on the sapphire crystal growth. For example, these models allow predicting the defects that occur due to local overcooling of crucible walls in the thermal node leading to the accelerated crystal growth. We also develop the prediction models for obtained crystal weight, blocks, cracks, bubbles formation, and total defect characteristics. The models were trained on all data sets and later tested for generalization on testing sets, which did not overlap the training set. During training and testing, we find the recall, precision of prediction and analyze the correlation among the features. The results have shown that the precision of neural network method for predicting defects formed by local overcooling of the crucible reached 0.94. |
| Databáze: | OpenAIRE |
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