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2篇 您的检索式：作者名="Benjamin P.MacLeod"

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		题名	作者	年代	出处	被引量
	1	A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics显示文摘We report a fast,reliable and non-destructive method for quantifying the homogeneity of perovskite thin films over large areas using machine vision.We adapt existing machine vision algorithms to spatially quantify multiple perovskite film properties(substrate coverage,film thickness,defect density)with pixel resolution from pictures of 25 cm2 samples.Our machine vision tool—called PerovskiteVision—can be combined with an optical model to predict photovoltaic cell and module current density from the perovskite film thickness.We use the measured film properties and predicted device current density to identify a posteriori the process conditions that simultaneously maximize the device performance and the manufacturing throughput for large-area perovskite deposition using gas-knife assisted slot-die coating.PerovskiteVision thus facilitates the transfer of a new deposition process to large-scale photovoltaic module manufacturing.This work shows how machine vision can accelerate slow characterization steps essential for the multi-objective optimization of thin film deposition processes.	Nina Taherimakhsousi Mathilde Fievez Benjamin P.MacLeod Edward P.Booker Emmanuelle Fayard Muriel Matheron Matthieu Manceau Stéphane Cros Solenn Berson Curtis P.Berlinguette	2021	npj Computational Materials2021，，1：	1
	2	Quantifying defects in thin films using machine vision显示文摘The sensitivity of thin-film materials and devices to defects motivates extensive research into the optimization of film morphology.This research could be accelerated by automated experiments that characterize the response of film morphology to synthesis conditions.Optical 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 analysis.Existing 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 conditions.Here,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 conditions.This CNN is readily adapted to new thin-film image analysis tasks and will facilitate the use of imaging in automated thin-film research systems.	Nina Taherimakhsousi Benjamin P.MacLeod Fraser G.L.Parlane Thomas D.Morrissey Edward P.Booker Kevan E.Dettelbach Curtis P.Berlinguette	2020	npj Computational Materials2020，，1：	1

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