Segregation of alloy and dopant atoms at defects in nitride materials

氮化物材料缺陷处合金和掺杂原子的偏析

• 批准号: EP/Y00423X/1
• 负责人: David Bowler
• 金额: $ 55.67万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY00423X%2F1
• 关键词: Segregation; alloy; dopant; atoms; defects

Electronic and opto-electronic devices based on gallium nitride (GaN) form a multi-billion dollar industry across the world, including lighting (LEDs), power sources and communications (radar and 5G). One of the most challenging aspects of developing these devices commercially is the high density of defects - i.e. mistakes in the crystal's structure - found in most commercially grown GaN, such as dislocations and stacking faults. It is possible to grow GaN with fewer of these mistakes, but it is slow and expensive, and most devices therefore contain a high density of defects, which will affect device performance.In particular, all devices will contain either alloying elements (aluminium gallium nitride, AlGaN, and indium gallium nitride, InGaN, are made by alloying Al and In with Ga during growth) and/or doping elements (magnesium, Mg, is added to change the conduction properties of GaN, for instance for making LEDs) and these elements will interact with the defects, which can prevent the extra elements from having the intended impact or change the local properties of the material being grown in undesirable ways.We will study how alloying and doping elements interact with defects, in particular where larger or smaller numbers of these atoms are found relative to what is expected. We will seek to understand why these changes happen, and ultimately how they can be controlled, either to reduce the numbers of defects, or to reduce the harmful effects of the defects on the desired materials properties.Our project will link state-of-the-art experimental techniques with cutting edge theory and modelling approaches. The experimental data will allow us to examine the positions of individual atoms with exquisite detail, while the modelling will address problems which involve large numbers of atoms, something which standard approaches cannot manage. This combination of techniques will enable us to understand and control the materials in ways that are not possible with each technique independently, and which will feed into industrial processes.

基于氮化镓 (GaN) 的电子和光电器件在全球形成了价值数十亿美元的产业，包括照明 (LED)、电源和通信（雷达和 5G）。商业化开发这些器件的最具挑战性的方面之一是在大多数商业生长的 GaN 中发现的高密度缺陷，即晶体结构中的错误，例如位错和堆垛层错。生长 GaN 时可以减少这些错误，但速度慢且成本高，因此大多数器件都含有高密度的缺陷，这会影响器件性能。特别是，所有器件都将含有合金元素（氮化铝镓） 、AlGaN 和氮化铟镓 (InGaN) 是通过在生长过程中将 Al 和 In 与 Ga 合金化而制成的）和/或掺杂元素（添加镁 (Mg) 以改变 GaN 的导电性能，例如用于制造 LED）和这些元素将与缺陷相互作用，这可以防止额外的元素产生预期的影响或改变以不良方式生长的材料的局部特性。我们将研究合金和掺杂元素如何与缺陷相互作用，特别是在较大或较大的缺陷处相对于预期，发现的这些原子的数量较少。我们将寻求了解为什么会发生这些变化，以及最终如何控制它们，以减少缺陷数量，或减少缺陷对所需材料性能的有害影响。我们的项目将链接当前的状态-具有前沿理论和建模方法的艺术实验技术。实验数据将使我们能够以精致的细节检查单个原子的位置，而建模将解决涉及大量原子的问题，这是标准方法无法解决的问题。这种技术的结合将使我们能够以每种技术单独不可能实现的方式理解和控制材料，并将融入工业流程。