Obtaining Shape from Scanning Electron Microscope using Hopfield Neural Network

2005 ◽  
Vol 16 (6) ◽  
pp. 715-725 ◽  
Author(s):  
Yuji Iwahori ◽  
Haruki Kawanaka ◽  
Shinji Fukui ◽  
Kenji Funahashi
Keyword(s):  
Neural Network ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Hopfield Neural Network ◽  
Scanning Electron

Performance enhancement of Scanning Electron Microscope using a Deep Convolutional Neural Network

2022 ◽  
Author(s):  
Suresh Panchal ◽  
Unnikrishnan Gopinathan ◽  
Suwarna Datar
Keyword(s):  
Neural Network ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Convolutional Neural Network ◽  
Performance Enhancement ◽  
Deep Convolutional Neural Network ◽  
Sem Images ◽  
Scan Rate ◽  
Charging Effect ◽  
Scanning Electron

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.

scholarly journals Neural Network for Enhancing Microscopic Resolution Based on Images from Scanning Electron Microscope

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2139
Author(s):  
Chia-Hung Dylan Tsai ◽  
Chia-Hao Yeh
Keyword(s):  
Neural Network ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Visual Information ◽  
Ground Truth ◽  
Optical Microscope ◽  
Average Error ◽  
Optical Images ◽  
On Chip ◽  
Scanning Electron

In this paper, an artificial neural network is applied for enhancing the resolution of images from an optical microscope based on a network trained with the images acquired from a scanning electron microscope. The resolution of microscopic images is important in various fields, especially for microfluidics because the measurements, such as the dimension of channels and cells, largely rely on visual information. The proposed method is experimentally validated with microfluidic structure. The images of structural edges from the optical microscope are blurred due to optical effects while the images from the scanning electron microscope are sharp and clear. Intensity profiles perpendicular to the edges and the corresponding edge positions determined by the scanning electron microscope images are plugged in a neural network as the input features and the output target, respectively. According to the results, the blurry edges of the microstructure in optical images can be successfully enhanced. The average error between the predicted channel position and ground truth is around 328 nanometers. The effects of the feature length are discussed. The proposed method is expected to significantly contribute to microfluidic applications, such as on-chip cell evaluation.

scholarly journals Neural Network for Nanoscience Scanning Electron Microscope Image Recognition

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammad Hadi Modarres ◽  
Rossella Aversa ◽  
Stefano Cozzini ◽  
Regina Ciancio ◽  
Angelo Leto ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Image Recognition ◽  
Scanning Electron Microscope Image ◽  
Microscope Image ◽  
Electron Microscope Image ◽  
Scanning Electron

scholarly journals Deep Learning for Super-Resolution in a Field Emission Scanning Electron Microscope

AI ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Gao ◽  
Ma ◽  
Huang ◽  
Hua ◽  
Lan
Keyword(s):  
Neural Network ◽  
Electron Microscope ◽  
High Resolution ◽  
Scanning Electron Microscope ◽  
Field Emission ◽  
Deep Neural Network ◽  
Super Resolution ◽  
Resolution Image ◽  
Short Period ◽  
Scanning Electron

A field emission scanning electron microscope (FESEM) is a complex scanning electron microscope with ultra-high-resolution image scanning, instant printing, and output storage capabilities. FESEMs have been widely used in fields such as materials science, biology, and medical science. However, owing to the balance between resolution and field of view (FOV), when locating a target using an FESEM, it is difficult to view specific details in an image with a large FOV and high resolution simultaneously. This paper presents a deep neural network to realize super-resolution of an FESEM image. This technology can effectively improve the resolution of the acquired image without changing the physical structure of the FESEM, thus resolving the constraint problem between the resolution and FOV. Experimental results show that the apply of a deep neural network only requires a single image acquired by an FESEM to be the input. A higher resolution image with a large FOV and excellent noise reduction is obtained within a short period of time. To verify the effect of the model numerically, we evaluated the image quality by using the peak signal-to-noise ratio value and structural similarity index value, which can reach 26.88 dB and 0.7740, respectively. We believe that this technology will improve the quality of FESEM imaging and be of significance in various application fields.

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