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High-performance graphene-based transistors crucially depend on the creation of the high-quality graphene-metal contacts. Here we report an approach for achieving ultralow contact resistance simply with optical lithography by engineering a metal-graphene interface. Note that a significant improvement with optical lithography for the contact-treated graphene device leads to a contact resistance as low as 150 Ω·μm. The residue-free sacrificial film impedes the photoresist from further doping graphene, and all of the source and drain contact regions defined by optical lithography remain intact. This approach, being compatible with complementary metal-oxide-semiconductor (CMOS) fabrication processes regardless of the source of graphene, would hold promise for the large-scale production of graphene-based transistors with optical lithography.

With the rapid development of image recognition technology, it has its applications in medical, security and other fields, but this technology has many areas to be improved, such as the accuracy of recognition, real-time and other issues, which have always been a hot research topic in this field. This article involves the development process of image recognition (traditional image processing, machine learning, deep learning), and focuses on the advantages and disadvantages of the popular YOLO algorithm in image recognition. Finally, it describes the technical problems faced by the current target detection technology and the corresponding solutions.

In the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object detection (SOD) in light field images that fuses focus and GrabCut. The method improves the light field focus calculation based on the spatial domain by performing secondary blurring processing on the focus image and effectively suppresses the focus information of out-of-focus areas in different focus images. Aiming at the redundancy of focus cues generated by multiple foreground images, we use the optimal single foreground image to generate focus cues. In addition, aiming at the fusion of various cues in the light field in complex scenes, the GrabCut algorithm is combined with the focus cue to guide the generation of color cues, which realizes the automatic saliency target segmentation of the image foreground. Extensive experiments are conducted on the light field dataset to demonstrate that our algorithm can effectively segment the salient target area and background area under the light field image, and the outline of the salient object is clear. Compared with the traditional GrabCut algorithm, the focus degree is used instead of artificial Interactively initialize GrabCut to achieve automatic saliency segmentation.