biostudies-literaturePotocnik TDepartment of Education and TrainingEuropean CommissionRoyal SocietyResearch Councils UKEngineering and Physical Sciences Research Council18009-18017https://www.ebi.ac.uk/biostudies/studies/S-EPMC9706672biostudies-literatureUnknown16(11)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.ACS nanoAutomated Computer Vision-Enabled Manufacturing of Nanowire Devices.PMC9706672EP/P005152/1EP/L016567/1716471EP/T008369/1EP/V055003/1EP/S019324/1Potocnik TBurton OJWilkinson TDChristopher PJAlexander-Webber JAMouthaan RJoyce HJAlbrow-Owen TJagadish CHofmann STan HHfalseAutomated Computer Vision-Enabled Manufacturing of Nanowire Devices.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.2022-01-01T00:00:00Z2022 Nov2025-04-26T09:49:24.792Z2025-04-06T13:08:07.025ZS-EPMC97066723616210010.1021/acsnano.2c08187