Deep-Learning-Assisted Focused Ion Beam Nanofabrication

• Published in: Nano letters Vol. 22; no. 7; pp. 2734 - 2739
• Main Authors: Buchnev, Oleksandr, Grant-Jacob, James A., Eason, Robert W., Zheludev, Nikolay I., Mills, Ben, MacDonald, Kevin F.
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 13.04.2022
• Subjects: Deep Learning; Letter; Reproducibility of Results; deep learning; nanofabrication; focused ion beam milling
• ISSN: 1530-6984; 1530-6992; 1530-6992
• DOI: 10.1021/acs.nanolett.1c04604

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.