Self-limited etching for atomic scale surface engineering of metals: understanding and design

金属原子级表面工程的自限蚀刻：理解和设计

• 批准号: 2212981
• 负责人: Philippe Sautet
• 金额: $ 34.89万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212981&HistoricalAwards=false
• 关键词: Self; limited; etching; atomic; scale

All solids are terminated by a surface. To control the composition and structure of a surface, chemically selective and spatially accurate modification processes with atomic-level precision are required. This is particularly true for the semiconductor industry where device feature sizes have entered the single-digit nanometer scale. To enable the fabrication of future nanodevices, this project seeks to develop methods to selectively and directionally etch industrially significant metals. Traditionally, metal etching and patterning has been performed by expositing the surface to an acid. However, such an approach cannot be used for nanofabrication because of its poor selectivity, non-directionality, and poor etch-depth control. Plasma etch processes constitute a significant improvement in directional control, but these processes can suffer from metal redeposition. Atomic layer etching (ALE) offers a promising alternative. ALE is a two-step self-limiting cyclic process where the metal surface is first modified by a plasma process and then is exposed to a precursor that selectively etches the modified surface. While having the potential to address all the aforementioned drawbacks of etch processes, a thorough understanding of the plasma and surface reaction mechanisms for important metals such as Ni and Cu is needed. This proposal seeks to fill this knowledge gap with a comprehensive computational chemistry approach. The project integrates research and training of Ph.D. and undergraduate students at the frontier of theoretical modeling and surface engineering process design and discovery. These students will be well prepared by the broad training on electronic structure calculations, algorithms of artificial intelligence and data science, surface chemistry experiments, and understanding of experimental data, capabilities, and limitations.This computational/experimental research program focuses on developing atomic layer etch (ALE) processes for the layer-by-layer removal of metal films. The reactive ALE process to be modelled starts with a metal surface modification step under plasma conditions to convert surface metal atoms to a surface compound that, when exposed to an etching agent, forms volatile metal-complexes that desorb, exposing the etched metal surface. The modification step will be modelled using molecular dynamics (MD) simulations with neural network potentials (NNPs), considering realistic initial kinetic energy for the trajectories. The NNP parameters will be identified using a training data set generated using density functional theory (DFT) based calculations. The results of plasma modification machine-learning MD simulations will be verified with surface modification experiments in an Inductively Coupled Plasma (ICP) chamber. For the etching reaction step, a thermodynamic database of energy will first be constructed as a function of etchant, substrate, modifiers, and process conditions. The thermodynamic database will be used to propose feasible etching chemistries. For experimentally validated cases, a detailed computational mechanistic exploration of reaction elementary steps for the etching reaction will determine the size of kinetic barriers for key low-energy pathways. Collectively, this methodology will result in an enhanced understanding of self-limiting surface reactions as well as the definition of optimal reactants to accurately engineer metallic surfaces. Educational and outreach activities supported by this program include partnering with the UCLA Center for Excellence in Engineering and Diversity (CEED) to identify top underrepresented minority (URM) students to work on this program (undergraduate student in the first year and high-school student in the second year). Informal science communication will be performed using the educational portals, such as Atomic Scale Design Network (ASDN.net) and NanoHUB (nanohub.org). The research team will create educational pages that bring forth the novel concepts and ideas in the field of atomic scale surface engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

所有实体都以曲面终止。为了控制表面的组成和结构，需要具有原子级精度的化学选择性和空间精确的改性工艺。这对于半导体工业尤其如此，其中器件特征尺寸已经进入个位数纳米尺度。为了能够制造未来的纳米器件，该项目旨在开发选择性和定向蚀刻工业上重要金属的方法。传统上，金属蚀刻和图案化是通过将表面与酸接触来进行的。然而，这种方法不能用于纳米纤维，因为其选择性差、无方向性和蚀刻深度控制差。等离子体蚀刻工艺构成方向控制的显著改进，但是这些工艺可能遭受金属再沉积。原子层蚀刻（ALE）提供了一个有前途的替代方案。ALE是一种两步自限循环工艺，其中金属表面首先通过等离子体工艺改性，然后暴露于选择性蚀刻改性表面的前体。虽然具有解决蚀刻工艺的所有上述缺点的潜力，但需要对重要金属（如Ni和Cu）的等离子体和表面反应机制有透彻的理解。该提案旨在通过全面的计算化学方法填补这一知识空白。该项目集研究和博士生培养于一体。和本科生在理论建模和表面工程工艺设计和发现的前沿。这些学生将通过电子结构计算，人工智能和数据科学算法，表面化学实验，以及对实验数据，能力和局限性的理解进行广泛的培训。这个计算/实验研究计划专注于开发原子层蚀刻（ALE）工艺，用于逐层去除金属薄膜。待建模的反应性ALE工艺开始于等离子体条件下的金属表面改性步骤，以将表面金属原子转化为表面化合物，当暴露于蚀刻剂时，该表面化合物形成挥发性金属络合物，该挥发性金属络合物解吸，暴露蚀刻的金属表面。修改步骤将使用具有神经网络势（NNPs）的分子动力学（MD）模拟进行建模，考虑轨迹的实际初始动能。将使用基于密度泛函理论（DFT）的计算生成的训练数据集识别NNP参数。等离子体改性机器学习MD模拟的结果将通过电感耦合等离子体（ICP）室中的表面改性实验进行验证。对于蚀刻反应步骤，首先将能量的热力学数据库构建为蚀刻剂、衬底、改性剂和工艺条件的函数。热力学数据库将用于提出可行的蚀刻化学。对于实验验证的情况下，蚀刻反应的反应基本步骤的详细计算机理探索将确定关键低能途径的动力学势垒的大小。总的来说，这种方法将加深对自限制表面反应的理解，以及准确设计金属表面的最佳反应物的定义。该计划支持的教育和推广活动包括与加州大学洛杉矶分校卓越工程和多样性中心（CEED）合作，以确定顶尖的代表性不足的少数民族（URM）学生参加该计划（第一年的本科生和第二年的高中生）。非正式的科学交流将使用教育门户网站进行，如原子尺度设计网络（ASDN.net）和NanoHUB（nanohub.org）。该研究团队将创建教育页面，提出原子尺度表面工程领域的新概念和想法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。