ArticleNano letters2022
Deep-Learning-Assisted Focused Ion Beam Nanofabrication.
Article in Nano letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 33 citations in OpenAlex.
- Nano bio-robots: a new frontier in targeted therapeutic delivery.Frontiers in robotics and AI · 2025Review
- Integrating deep convolutional surrogate solvers and particle swarm optimization for efficient inverse design of plasmonic patch nanoantennas.Nanophotonics (Berlin, Germany) · 2024Article
- Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review.Materials (Basel, Switzerland) · 2024Review
- Machine learning to optimize additive manufacturing for visible photonics.Nanophotonics (Berlin, Germany) · 2023Article
- The 3D Controllable Fabrication of Nanomaterials with FIB-SEM Synchronization Technology.Nanomaterials (Basel, Switzerland) · 2023Review
- Concurrently Fabricating Precision Meso- and Microscale Cross-Scale Arrayed Metal Features and Components by Using Wire-Anode Scanning Electroforming Technique.Micromachines · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.