Deep-Learning-Assisted Focused Ion Beam Nanofabrication.

Deep-Learning-Assisted Focused Ion Beam Nanofabrication.
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深度学习辅助聚焦的离子束纳米化。

DOI:
10.1021/acs.nanolett.1c04604
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发表时间:
2022-04-13
期刊:
影响因子:
10.8
通讯作者:
MacDonald KF
MacDonald KF
中科院分区:
材料科学1区
文献类型:
--
作者:
Buchnev O;Grant-Jacob JA;Eason RW;Zheludev NI;Mills B;MacDonald KF

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聚焦离子束(FIB)铣削是微纳米制造、器件和材料表征的重要快速成型工具。它允许在各种各样的材料中制造任意结构,但是为给定任务建立工艺参数是一个多维优化挑战,通常通过耗时,反复的试错来解决。在这里,我们表明,从先前的制造经验中深度学习可以预测由聚焦离子束(FIB)铣削制造的结构的后期外观,在离子束参数范围内,考虑到仪器和目标特定的伪影,精度为bb0 96%。由于预测只需几毫秒,该方法可以在接近实时的情况下部署,以加快优化并提高FIB处理的可重复性。
Focused ion beam (FIB) milling is an important rapid prototyping tool for micro- and nanofabrication and device and materials characterization. It allows for the manufacturing of arbitrary structures in a wide variety of materials, but establishing the process parameters for a given task is a multidimensional optimization challenge, usually addressed through time-consuming, iterative trial-and-error. Here, we show that deep learning from prior experience of manufacturing can predict the postfabrication appearance of structures manufactured by focused ion beam (FIB) milling with >96% accuracy over a range of ion beam parameters, taking account of instrument- and target-specific artifacts. With predictions taking only a few milliseconds, the methodology may be deployed in near real time to expedite optimization and improve reproducibility in FIB processing.
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