| Abstract: | Graphene has attracted a lot of interest since its discovery. However, graphene layers made by mechanical exfoliation need to be carefully distinguished from multi-layer graphite and residues by experienced experts, which is time consuming and requires significant experience. In this thesis, an image segmentation method based on deep learning is developed to identify single-layer graphene (SLG) under an optical microscope. By introducing a modified UNet++ with an attention gate and a residue network (ResNet) for further classification as a two-level structure, we can distinguish SLG from graphite with high accuracy by using only a small amount of training images. In addition, a graphene spin device is fabricated through magnetron sputtering in the experiment. By applying transverse and longitudinal current on the device, the interplay between thermal spin current and charge spin current is investigated. The non-local spin voltage is enhanced by the thermal spin injection and the thermal spin voltage reaches the maximum close to the Dirac point which make graphene a promising material for the thermoelectric spin device in the future due to its long spin lifetime and spin diffusion length. The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/197864 |
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