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

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 q...

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Bibliographic Details
Published in:	Microsystems & nanoengineering Vol. 8; no. 1; p. 19
Main Authors:	Zhang, Jian, Perrin, Mickael L., Barba, Luis, Overbeck, Jan, Jung, Seoho, Grassy, Brock, Agal, Aryan, Muff, Rico, Brönnimann, Rolf, Haluska, Miroslav, Roman, Cosmin, Hierold, Christofer, Jaggi, Martin, Calame, Michel
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 10-02-2022 Springer Nature B.V Nature Publishing Group
Subjects:	639/301/357/73 639/925/357/73 Artificial neural networks Carbon Carbon nanotubes Classification Deep learning Electronic systems Engineering High speed Machine learning Metallicity Nanomaterials Nanotechnology Nanotubes Neural networks Quality control Raman spectra Raman spectroscopy Signal to noise ratio Spectrum analysis Workflow Carbon nanotubes and fullerenes
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2055-7434 2096-1030 2055-7434
DOI:	10.1038/s41378-022-00350-w