Improving the dipping step in czochraski process using haar-cascade algorithm

Abstract

Czochralski crystal growth has become a popular technique to produce pure single crystals. Many methods have also been developed to optimize this process. In this study, a charge-coupled device camera was used to record the crystal growth progress from beginning to end. The device outputs images which were then used to create a classifier using the Haar-cascade and AdaBoost algorithms. After the classifier was generated, artificial intelligence (AI) was used to recognize the images obtained from good dipping and calculate the duration of this operating. This optimization approach improved a Czochralski which can detect a good dipping step automatically and measure the duration with high accuracy. Using this development, the labor cost of the Czochralski system can be reduced by changing the contribution of human specialists’ mission.