{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lee W"],"funding":["COXEM","National Research Foundation of Korea"],"pagination":["20933"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8536763"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(1)"],"pubmed_abstract":["Scanning electron microscopy (SEM) is a high-resolution imaging technique with subnanometer spatial resolution that is widely used in materials science, basic science, and nanofabrication. However, conducting SEM is rather complex due to the nature of using an electron beam and the many parameters that must be adjusted to acquire high-quality images. Only trained operators can use SEM equipment properly, meaning that the use of SEM is restricted. To broaden the usability of SEM, we propose an autofocus method for a SEM system based on a dual deep learning network, which consists of an autofocusing-evaluation network (AENet) and an autofocusing-control network (ACNet). The AENet was designed to evaluate the quality of given images, with scores ranging from 0 to 9 regardless of the magnifica"],"journal":["Scientific reports"],"pubmed_title":["Robust autofocusing for scanning electron microscopy based on a dual deep learning network."],"pmcid":["PMC8536763"],"funding_grant_id":["G01200373","2020R1A2C300674512"],"pubmed_authors":["Nam HS","Kim YJ","Kim YG","Yoo H","Lee W","Lee JH"],"additional_accession":[]},"is_claimable":false,"name":"Robust autofocusing for scanning electron microscopy based on a dual deep learning network.","description":"Scanning electron microscopy (SEM) is a high-resolution imaging technique with subnanometer spatial resolution that is widely used in materials science, basic science, and nanofabrication. However, conducting SEM is rather complex due to the nature of using an electron beam and the many parameters that must be adjusted to acquire high-quality images. Only trained operators can use SEM equipment properly, meaning that the use of SEM is restricted. To broaden the usability of SEM, we propose an autofocus method for a SEM system based on a dual deep learning network, which consists of an autofocusing-evaluation network (AENet) and an autofocusing-control network (ACNet). The AENet was designed to evaluate the quality of given images, with scores ranging from 0 to 9 regardless of the magnifica","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2025-04-04T13:16:47.873Z","creation":"2025-04-04T13:16:47.873Z"},"accession":"S-EPMC8536763","cross_references":{"pubmed":["34686722"],"doi":["10.1038/s41598-021-00412-5"]}}