Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition  

作　　者：Sumner B.Harris Christopher M.Rouleau Kai Xiao Rama K.Vasudevan 

机构地区：[1]Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA

出　　处：《npj Computational Materials》2024年第1期2162-2169,共8页计算材料学(英文)

基　　金：supported by the Center for Nanophase Materials Sciences(CNMS),which is a US Department of Energy,Office of Science User Facility at Oak Ridge National Laboratory;supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC05-00OR22725.

摘　　要：Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties.Pulsed laser deposition(PLD)is emerging as a viable autonomous synthesis tool,and so the need arises to develop machine learning(ML)techniques that are capable of extracting information from in situ diagnostics.Here,we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics.

关 键 词：PREDICTION IMAGE SEQUENCES 

分 类 号：TN2[电子电信—物理电子学]