Abstract
It is of great importance to pick up a single carbon nanotube (CNT) from a bulk of CNTs for nanodevice fabrication. In this study, we have proposed a nanorobotic manipulation system allowing automated pick-up of CNTs based on visual feedback. We utilize histogram normalization for automatic binarization, and it achieves to clearly distinguish CNTs from substrate and other impurities under different image brightness. Furthermore, we develop the gradient orientation inversion (GOI) algorithm to recognize CNT tip and atomic force microscopy (AFM) cantilever. Taking full advantages of the geometrical characteristics of CNT and AFM cantilever, GOI is proved to be quite robust. We have designed segment detection method (SDM) to successfully separate the AFM cantilever and CNT, whereas the contact detection between them is achieved by analyzing the straightness variation. Preliminary experimental results imply that our method shows high promise in realistic fabrication of nanodevices.
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