Tadokoro Yukihiro, Funayama Keita, Kawano Keisuke, Miura Atsushi, Hirotani Jun, Ohno Yutaka, Tanaka Hiroya
Toyota Central R&D Labs., Inc., Nagakute, Aichi Japan.
Toyota Research Institute of North America, Ann Arbor, MI USA.
Microsyst Nanoeng. 2023 Mar 22;9:32. doi: 10.1038/s41378-023-00507-1. eCollection 2023.
Nanoscale cantilevers (nanocantilevers) made from carbon nanotubes (CNTs) provide tremendous benefits in sensing and electromagnetic applications. This nanoscale structure is generally fabricated using chemical vapor deposition and/or dielectrophoresis, which contain manual, time-consuming processes such as the placing of additional electrodes and careful observation of single-grown CNTs. Here, we demonstrate a simple and Artificial Intelligence (AI)-assisted method for the efficient fabrication of a massive CNT-based nanocantilever. We used randomly positioned single CNTs on the substrate. The trained deep neural network recognizes the CNTs, measures their positions, and determines the edge of the CNT on which an electrode should be clamped to form a nanocantilever. Our experiments demonstrate that the recognition and measurement processes are automatically completed in 2 s, whereas comparable manual processing requires 12 h. Notwithstanding the small measurement error by the trained network (within 200 nm for 90% of the recognized CNTs), more than 34 nanocantilevers were successfully fabricated in one process. Such high accuracy contributes to the development of a massive field emitter using the CNT-based nanocantilever, in which the output current is obtained with a low applied voltage. We further showed the benefit of fabricating massive CNT-nanocantilever-based field emitters for neuromorphic computing. The activation function, which is a key function in a neural network, was physically realized using an individual CNT-based field emitter. The introduced neural network with the CNT-based field emitters recognized handwritten images successfully. We believe that our method can accelerate the research and development of CNT-based nanocantilevers for realizing promising future applications.
由碳纳米管（CNT）制成的纳米级悬臂（纳米悬臂）在传感和电磁应用中具有巨大优势。这种纳米级结构通常采用化学气相沉积和/或介电泳法制造，其中包含人工操作、耗时的过程，如放置额外电极以及仔细观察单个生长的碳纳米管。在此，我们展示了一种简单且人工智能（AI）辅助的方法，用于高效制造大量基于碳纳米管的纳米悬臂。我们使用了在基板上随机定位的单个碳纳米管。经过训练的深度神经网络识别碳纳米管，测量它们的位置，并确定应在其上夹设电极以形成纳米悬臂的碳纳米管边缘。我们的实验表明，识别和测量过程在2秒内自动完成，而类似的人工处理则需要12小时。尽管经过训练的网络测量误差较小（90%被识别的碳纳米管误差在200纳米以内），但在一个过程中成功制造出了超过34个纳米悬臂。如此高的精度有助于开发基于碳纳米管纳米悬臂的大量场发射器，其中在低施加电压下即可获得输出电流。我们还展示了制造大量基于碳纳米管 - 纳米悬臂的场发射器用于神经形态计算的优势。神经网络中的关键函数激活函数，通过基于单个碳纳米管的场发射器在物理上得以实现。引入的带有基于碳纳米管场发射器的神经网络成功识别了手写图像。我们相信，我们的方法能够加速基于碳纳米管的纳米悬臂的研发，以实现未来有前景的应用。
