Performance enhancement of a scanning electron microscope using a deep convolutional neural network

Abstract

We report noise reduction and image enhancement in Scanning Electron Microscope (SEM) imaging while maintaining a Fast-Scan rate during imaging, using a Deep Convolutional Neural Network (D-CNN). SEM images of non-conducting samples without conducting coating always suffer from charging phenomenon, giving rise to SEM images with low contrast or anomalous contrast and permanent damage to the sample. One of the ways to avoid this effect is to use Fast-Scan mode, which suppresses the charging effect fairly well. Unfortunately, this also introduces noise and gives blurred images. The D-CNN has been used to predict relatively noise-free images as obtained from a Slow-Scan from a noisy, Fast-Scan image. The predicted images from D-CNN have the sharpness of images obtained from a Slow-Scan rate while reducing the charging effect due to images obtained from Fast-Scan rates. We show that using the present method, and it is possible to increase the scanning rate by a factor of about seven with an output of image quality comparable to that of the Slow-Scan mode. We present experimental results in support of the proposed method.

Improved multi-scale convolutional neural network about image defogging method

“…It was not until Hinton et al published an article in《Science》 in 2006 that pointed out that multilayer perceptrons have superior perceptual feature capabilities, and CNN awakened again. Deep neural networks [3] have come a long way since 2012, when AlexNet defended its title at the ImageNet image recognition competition for the first time with a first-place finish and an accuracy of 10% over the second-place finisher.…”