Open AccessAutomated Computer Vision-Enabled Manufacturing of Nanowire Devices
Author(s) -
Teja Potočnik,
Peter J. Christopher,
Ralf Mouthaan,
Tom Albrow-Owen,
Oliver J. Burton,
Chennupati Jagadish,
Hark Hoe Tan,
Timothy D. Wilkinson,
Stephan Hofmann,
Hannah J. Joyce,
Jack A. Alexander-Webber
Publication year - 2022
Publication title -
acs nano
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 5.554
H-Index - 382
eISSN - 1936-086X
pISSN - 1936-0851
DOI - 10.1021/acsnano.2c08187
Subject(s) - nanowire , nanodevice , materials science , nanotechnology , throughput , nanostructure , automation , pixel , computer science , microscopy , fabrication , bottleneck , artificial intelligence , embedded system , wireless , optics , engineering , mechanical engineering , medicine , telecommunications , physics , alternative medicine , pathology
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.