A high-speed atomic force microscopy with super resolution based on path planning scanning.

An atomic force microscopy generally adopts a raster scanning method to obtain the image of the sample morphology. However, the raster method takes too much time on the base part without focusing enough on the object, thereby restricting the scanning speed of an AFM. To solve this problem, this paper proposes a novel path planning based scanning method to achieve high-speed scanning with super resolution for AFMs. Specifically speaking, a fast scanning process is first carried out to generate a low-resolution image with less time, then a convolutional neural network is designed to construct a super-resolution image based on the fast scanning image. Afterwards, an advanced detection algorithm is proposed to achieve the accurate object detection and localization. Furthermore, an improved ant colony optimization algorithm is proposed to realize the path planning for scanning the objects with high quality, whose imaging result is then matched with the previous super-resolution image to construct the entire sample image, thus achieving fast scanning with super resolution. Experimental and application results demonstrate the good performance of the proposed scanning method.