{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Potocnik T"],"funding":["Department of Education and Training","European Commission","Royal Society","Research Councils UK","Engineering and Physical Sciences Research Council"],"pagination":["18009-18017"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9706672"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(11)"],"pubmed_abstract":["We present a high-throughput method for identifying and characterizing individual nanowires and for automatically designing electrode patterns with high alignment accuracy. Central to our method is an optimized machine-readable, lithographically processable, and multi-scale fiducial marker system─dubbed LithoTag─which provides nanostructure position determination at the nanometer scale. A grid of uniquely defined LithoTag markers patterned across a substrate enables image alignment and mapping in 100% of a set of >9000 scanning electron microscopy (SEM) images (>7 gigapixels). Combining this automated SEM imaging with a computer vision algorithm yields location and property data for individual nanowires. Starting with a random arrangement of individual InAs nanowires with diameters of 30 ± 5 nm on a single chip, we automatically design and fabricate >200 single-nanowire devices. For >75% of devices, the positioning accuracy of the fabricated electrodes is within 2 pixels of the original microscopy image resolution. The presented LithoTag method enables automation of nanodevice processing and is agnostic to microscopy modality and nanostructure type. Such high-throughput experimental methodology coupled with data-extensive science can help overcome the characterization bottleneck and improve the yield of nanodevice fabrication, driving the development and applications of nanostructured materials."],"journal":["ACS nano"],"pubmed_title":["Automated Computer Vision-Enabled Manufacturing of Nanowire Devices."],"pmcid":["PMC9706672"],"funding_grant_id":["EP/P005152/1","EP/L016567/1","716471","EP/T008369/1","EP/V055003/1","EP/S019324/1"],"pubmed_authors":["Potocnik T","Burton OJ","Wilkinson TD","Christopher PJ","Alexander-Webber JA","Mouthaan R","Joyce HJ","Albrow-Owen T","Jagadish C","Hofmann S","Tan HH"],"additional_accession":[]},"is_claimable":false,"name":"Automated Computer Vision-Enabled Manufacturing of Nanowire Devices.","description":"We present a high-throughput method for identifying and characterizing individual nanowires and for automatically designing electrode patterns with high alignment accuracy. Central to our method is an optimized machine-readable, lithographically processable, and multi-scale fiducial marker system─dubbed LithoTag─which provides nanostructure position determination at the nanometer scale. A grid of uniquely defined LithoTag markers patterned across a substrate enables image alignment and mapping in 100% of a set of >9000 scanning electron microscopy (SEM) images (>7 gigapixels). Combining this automated SEM imaging with a computer vision algorithm yields location and property data for individual nanowires. Starting with a random arrangement of individual InAs nanowires with diameters of 30 ± 5 nm on a single chip, we automatically design and fabricate >200 single-nanowire devices. For >75% of devices, the positioning accuracy of the fabricated electrodes is within 2 pixels of the original microscopy image resolution. The presented LithoTag method enables automation of nanodevice processing and is agnostic to microscopy modality and nanostructure type. Such high-throughput experimental methodology coupled with data-extensive science can help overcome the characterization bottleneck and improve the yield of nanodevice fabrication, driving the development and applications of nanostructured materials.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-26T09:49:24.792Z","creation":"2025-04-06T13:08:07.025Z"},"accession":"S-EPMC9706672","cross_references":{"pubmed":["36162100"],"doi":["10.1021/acsnano.2c08187"]}}