坩埚(大地测量学)
杂质
微下拉
硅
氧气
极限氧浓度
Crystal(编程语言)
单晶硅
材料科学
分析化学(期刊)
蒸发
晶体生长
矿物学
温度梯度
冶金
化学
结晶学
热力学
色谱法
物理
计算化学
有机化学
量子力学
程序设计语言
计算机科学
作者
Jiancheng Li,Zaoyang Li,Lijun Liu,Changzhen Wang,Yuqi Jin
标识
DOI:10.1016/j.jcrysgro.2023.127180
摘要
The continuous-feeding Czochralski method (CCz) is an effective and promising way for the high-quality and large-weight monocrystalline silicon growth. In the CCz technology, the inner crucible is employed to prevent unmelted silicon granules from being transported to the melt-crystal interface, which introduces a new source of oxygen impurities and makes the temperature and flow distribution in the melt complicated. So it is challenging to regulate the oxygen impurities at the m-c interface in the CCz process. The melt depth stays constant during the CCz process, and it determines the crucible area where oxygen impurities are dissolved. Therefore, in order to manage and minimize the oxygen impurities at the m-c interface, controlling the melt depth may be a good way. In the study, the influence of melt depth on oxygen impurity distribution at the melt-crystal interface is investigated using a series of global simulations coupled with the transport of oxygen and carbon impurities. The findings indicate that as melt depth increases, the temperature of the crucible wall inside the inner crucible drops, and oxygen impurities transported from the melt between the inner and outer crucibles to the interior of the inner crucible likewise decrease in content. However, the rate of SiO evaporation at the free melt surface inside the inner crucible essentially stays the same. Therefore, the oxygen impurity content at the melt-crystal (m-c) interface decreases with the increase of melt depth. The paper also suggests that while constructing the scheme for lowering oxygen content, it is necessary to take into account the position relation between the heater and the crucible, the height of the crucible above the free melt surface, and the rotation of the inner crucible.
科研通智能强力驱动
Strongly Powered by AbleSci AI