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
Laboratory for Transport at Nanoscale Interfaces, Empa, Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Dübendorf, Switzerland.
Machine Learning and Optimization Laboratory, School of Computer and Communication Sciences, EPFL, CH-1015 Lausanne, Switzerland.
Microsyst Nanoeng. 2022 Feb 10;8:19. doi: 10.1038/s41378-022-00350-w. eCollection 2022.
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.
识别具有节能电子系统所需特性的纳米材料通常是一项繁琐的人工任务。在纳米材料集成到大规模制造过程的各个阶段，快速定位和表征纳米材料的工作流程对于质量控制以及最终它们在工业上的应用至关重要。在这项工作中，我们开发了一种高通量方法，通过使用高速拉曼成像和深度学习分析来快速识别悬浮的碳纳米管（CNT）。即使对于信噪比（SNR）极低至0.9的拉曼光谱，我们也能实现超过90%的分类准确率，而当SNR为2.2时，准确率达到98%。通过在优化的卷积神经网络（CNN）的softmax层输出上应用阈值，我们进一步提高了分类的准确性。此外，我们提出了一种优化的拉曼扫描策略，以在识别每个样品上碳纳米管的位置、数量和金属性的同时，将采集时间减至最短。我们的方法可以很容易地扩展到其他类型的纳米材料，并有可能集成到生产线中，以在制造过程中监测纳米材料的质量和特性。
