Here, we report a successfully modified Czochralski process system by introducing the cooling system and subsequent examination of the results using crystal growth simulation analysis. Two types of cooling system models have been designed, i.e., long type and double type cooling design (LTCD and DTCD) and their production quality of monocrystalline silicon ingot was compared with that of the basic type cooling design (BTCD) system. The designed cooling system improved the uniformity of the temperature gradient in the crystal and resulted in the significant decrease of the thermal stress. Moreover, the silicon monocrystalline ingot growth rate has been enhanced to 18% by using BTCD system. The detailed simulation results have been discussed in the manuscript. The present research demonstrates that the proposed cooling system would stand as a promising technique to be applied in CZ-Si crystal growth with a large size/high pulling rate.

There are many ways to maintain the safety of workers on a working site, such as using a human supervisor, computer supervisor, and smoke–flame detecting system. In order to create a safety warning system for the working site, the machine-learning algorithm—Haar-cascade classifier—was used to build four different classes for safety equipment recognition. Then a proposed algorithm was applied to calculate a score to determine the dangerousness of the current working environment based on the safety equipment and working environment. With this data, the system decides whether it is necessary to give a warning signal. For checking the efficiency of this project, three different situations were installed with this system. Generally, with the promising outcome, this application can be used in maintaining, supervising, and controlling the safety of a worker.

In this paper, we report a successfully modified single-crystal Si growth furnace for impurity control. Four types of arbitrary magnetic heater (AMGH) systems with 3, 4, 5, and poly parts were designed in a coil shape and analyzed using crystal growth simulation. The concentration of oxygen impurities in single-crystal Si ingots was compared among the designed AMGHs and a normal graphite heater (NGH). The designed AMGHs were confirmed to be able to control turbulence and convection in a molten state, which created a vortex that influenced the oxygen direction near the melt–crystal interface. It was confirmed that replacing NGH with AMGHs resulted in a reduction in the average oxygen concentration at the Si melt–crystal interface by approximately 4.8%.

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.