Quantifying defects in thin films using machine vision
作者机构：Department of ChemistryThe University of British Columbia2036 Main MallVancouverBC V6T 1Z1Canada Stewart Blusson Quantum Matter InstituteThe University of British Columbia2355 East MallVancouverBC V6T 1Z4Canada Department of Chemical&Biological EngineeringThe University of British Columbia2360 East MallVancouverBC V6T 1Z3Canada Canadian Institute for Advanced Research(CIFAR)MaRS Innovation Centre661 University Ave.Suite 505TorontoON M5G 1M1Canada
出 版 物：《npj Computational Materials》 (计算材料学（英文）)
年 卷 期：2020年第6卷第1期
页 面：737-742页
核心收录：
学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学]
基 金：The authors thank Natural Resources Canada(EIP2-MAT-001)for their financial support.C.P.B.is grateful to the Canadian Natural Sciences and Engineering Research Council(RGPIN 337345-13) Canadian Foundation for Innovation(229288) Canadian Institute for Advanced Research(BSE-BERL-162173) Canada Research Chairs for financial support
摘 要：The sensitivity of thin-film materials and devices to defects motivates extensive research into the optimization of film *** research could be accelerated by automated experiments that characterize the response of film morphology to synthesis *** imaging can resolve morphological defects in thin films and is readily integrated into automated experiments but the large volumes of images produced by such systems require automated *** approaches to automatically analyzing film morphologies in optical images require application-specific customization by software experts and are not robust to changes in image content or imaging ***,we present a versatile convolutional neural network(CNN)for thin-film image analysis which can identify and quantify the extent of a variety of defects and is applicable to multiple materials and imaging *** CNN is readily adapted to new thin-film image analysis tasks and will facilitate the use of imaging in automated thin-film research systems.
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