High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

作     者：Jian Zhang Mickael L.Perrin Luis Barba Jan Overbeck Seoho Jung Brock Grassy Aryan Agal Rico Muff Rolf Brönnimann Miroslav Haluska Cosmin Roman Christofer Hierold Martin Jaggi Michel Calame 

作者机构：Laboratory for Transport at Nanoscale InterfacesEmpaSwiss Federal Laboratories for Materials Science and TechnologyCH-8600 DübendorfSwitzerland Machine Learning and Optimization LaboratorySchool of Computer and Communication SciencesEPFLCH-1015 LausanneSwitzerland Department of Physics and Swiss Nanoscience InstituteUniversity of BaselCH-4056 BaselSwitzerland Micro-and NanosystemsDepartment of Mechanical and Process EngineeringETH ZurichCH-8092 ZurichSwitzerland 

出 版 物：《Microsystems & Nanoengineering》 (微系统与纳米工程（英文）)

年 卷 期：2022年第8卷第1期

页      面：259-267页

核心收录：

学科分类：07[理学] 070205[理学-凝聚态物理] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：We acknowledge financial support from Strategic Focus Area(SFA)Advanced Manufacturing(Project NanoAssembly) M.L.P.and J.Z.acknowledge funding by the EMPAPOSTDOCS-II program,which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska–Curie Grant Agreement no.754364 M.L.P.also acknowledges funding from the Swiss National Science Foundation under Spark grant no.196795 

主　　题：carbon suspended speed 

摘      要：The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task.A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-scale fabrication processes is essential for quality control and,ultimately,their industrial *** this work,we develop a high-throughput approach to rapidly identify suspended carbon nanotubes(CNTs)by using high-speed Raman imaging and deep learning *** for Raman spectra with extremely low signal-to-noise ratios(SNRs)of 0.9,we achieve a classification accuracy that exceeds 90%,while it reaches 98%for an SNR of *** applying a threshold on the output of the softmax layer of an optimized convolutional neural network(CNN),we further increase the accuracy of the ***,we propose an optimized Raman scanning strategy to minimize the acquisition time while simultaneously identifying the position,amount,and metallicity of CNTs on each *** approach can readily be extended to other types of nanomaterials and has the potential to be integrated into a production line to monitor the quality and properties of nanomaterials during fabrication.