A Light-Weight Convolutional Neural Network for Super-Resolving SEM Images to Enhance Real-Time Micro-Nano Manipulation

This paper presents a light-weight convolutional neural network for SEM imaging. Combining nanorobotic manipulation systems with the scanning electron microscope makes precise micro-nano manipulation based on visual feedback become possible. However, the contradiction between imaging speed and resolution hinders the real-time of micro-nano manipulation, which further obstacles to the realization of applying it in mass manufacturing situations. In this paper, we propose a super resolution method called SRSEMNet1 to enhance the imaging quality of SEM by super-resolving low resolution SEM images. In this way, high-quality SEM images could be obtained while consumes less time than acquiring them straightly from SEM, which contributes to the real time of micro-nano manipulation. We analyze the residual learning, skip connections, CBAM, and different loss functions to optimize the network. The method was tested in both well accepted benchmark dataset Urban100 and our own made dataset SSRID for SEM imaging.