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 sta...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 adoption.In this work,we develop a high-throughput approach to rapidly identify suspended carbon nanotubes(CNTs)by using high-speed Raman imaging and deep learning analysis.Even 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 2.2.By applying a threshold on the output of the softmax layer of an optimized convolutional neural network(CNN),we further increase the accuracy of the classification.Moreover,we propose an optimized Raman scanning strategy to minimize the acquisition time while simultaneously identifying the position,amount,and metallicity of CNTs on each sample.Our 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.展开更多
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.754364M.L.P.also acknowledges funding from the Swiss National Science Foundation under Spark grant no.196795。
文摘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 adoption.In this work,we develop a high-throughput approach to rapidly identify suspended carbon nanotubes(CNTs)by using high-speed Raman imaging and deep learning analysis.Even 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 2.2.By applying a threshold on the output of the softmax layer of an optimized convolutional neural network(CNN),we further increase the accuracy of the classification.Moreover,we propose an optimized Raman scanning strategy to minimize the acquisition time while simultaneously identifying the position,amount,and metallicity of CNTs on each sample.Our 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.
