| Autor: |
Buchnev O; Optoelectronics Research Centre, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom., Grant-Jacob JA; Optoelectronics Research Centre, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom., Eason RW; Optoelectronics Research Centre, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom., Zheludev NI; Optoelectronics Research Centre, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom.; Centre for Disruptive Photonic Technologies & The Photonics Institute, SPMS, Nanyang Technological University, Singapore 637371, Singapore., Mills B; Optoelectronics Research Centre, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom., MacDonald KF; Optoelectronics Research Centre, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom. |
| Abstrakt: |
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. |
