4.4 Article

Avoiding Gallium Pollution in Close-Coupled Showerhead Reactors, Alternative Process Routes

出版社

WILEY-V C H VERLAG GMBH
DOI: 10.1002/pssa.202200824

关键词

close-coupled showerhead; gallium pollution; InAlN; MOCVD; metal-organic vapor-phase epitaxy (MOVPE)

向作者/读者索取更多资源

In this study, the problem of gallium pollution in the InAlN layer is addressed by examining changes to process conditions. Using a model that combines GaN decomposition, gallium desorption, and diffusion, different process conditions are tested to limit desorption or diffusion. The results show that reducing the growth temperature and increasing the wafer-showerhead distance can both effectively reduce gallium pollution. Additionally, using AlGaN layers as the channel eliminates gallium pollution even at low compositions.
In contrast to the previous work which solved the problem of gallium pollution using hardware modifications, herein, the changes are examined to process conditions to reduce gallium pollution in InAlN layer. Using a model of GaN decomposition followed by gallium desorption and diffusion to the showerhead, different process conditions are used to limit either the desorption or the diffusion. Reducing the GaN growth temperature gives some improvement by reducing desorption, but affects channel mobility, likely due to increased carbon incorporation. Increasing the wafer-showerhead distance also reduces the gallium pollution, this time by a factor of around 2. Finally, using AlGaN layers instead of GaN as the channel completely removes gallium pollution, even for composition as low as 5%. It is suggested that in addition to reducing desorption due to alloying, the aluminum precursor may be acting as a getter for this gallium, and so stops it getting to the showerhead. A combination of these different approaches suggests that process conditions can significantly reduce gallium pollution in close-coupled showerhead reactors.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据