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Directing Matter: Toward Atomic-Scale 3D Nanofabrication

纳米技术 纳米光刻 材料科学 原子单位 Petascale计算 计算机科学 工程物理 制作 物理 超级计算机 量子力学 医学 操作系统 病理 替代医学
作者
Stephen Jesse,Albina Y. Borisevich,Jason D. Fowlkes,Andrew R. Lupini,Philip D. Rack,Raymond R. Unocic,Bobby G. Sumpter,Sergei V. Kalinin,Alex Belianinov,Olga S. Ovchinnikova
出处
期刊:ACS Nano [American Chemical Society]
卷期号:10 (6): 5600-5618 被引量:115
标识
DOI:10.1021/acsnano.6b02489
摘要

Enabling memristive, neuromorphic, and quantum-based computing as well as efficient mainstream energy storage and conversion technologies requires the next generation of materials customized at the atomic scale. This requires full control of atomic arrangement and bonding in three dimensions. The last two decades witnessed substantial industrial, academic, and government research efforts directed toward this goal through various lithographies and scanning-probe-based methods. These technologies emphasize 2D surface structures, with some limited 3D capability. Recently, a range of focused electron- and ion-based methods have demonstrated compelling alternative pathways to achieving atomically precise manufacturing of 3D structures in solids, liquids, and at interfaces. Electron and ion microscopies offer a platform that can simultaneously observe dynamic and static structures at the nano- and atomic scales and also induce structural rearrangements and chemical transformation. The addition of predictive modeling or rapid image analytics and feedback enables guiding these in a controlled manner. Here, we review the recent results that used focused electron and ion beams to create free-standing nanoscale 3D structures, radiolysis, and the fabrication potential with liquid precursors, epitaxial crystallization of amorphous oxides with atomic layer precision, as well as visualization and control of individual dopant motion within a 3D crystal lattice. These works lay the foundation for approaches to directing nanoscale level architectures and offer a potential roadmap to full 3D atomic control in materials. In this paper, we lay out the gaps that currently constrain the processing range of these platforms, reflect on indirect requirements, such as the integration of large-scale data analysis with theory, and discuss future prospects of these technologies.
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