Feature evaluation of complex hysteresis smoothing and its practical applications to noisy SEM images

Quality of a scanning electron microscopy (SEM) image is strongly influenced by noise. This is a fundamental drawback of the SEM instrument. Complex hysteresis smoothing (CHS) has been previously developed for noise removal of SEM images. This noise removal is performed by monitoring and processing properly the amplitude of the SEM signal. As it stands now, CHS may not be so utilized, though it has several advantages for SEM. For example, the resolution of image processed by CHS is basically equal to that of the original image. In order to find wide application of the CHS method in microscopy, the feature of CHS, which has not been so clarified until now is evaluated correctly. As the application of the result obtained by the feature evaluation, cursor width (CW), which is the sole processing parameter of CHS, is determined more properly using standard deviation of noise Nσ. In addition, disadvantage that CHS cannot remove the noise with excessively large amplitude is improved by a certain postprocessing. CHS is successfully applicable to SEM images with various noise amplitudes. SCANNING 35:292‐301, 2013. © 2012 Wiley Periodicals, Inc     

New generation scanning electron microscopy technology based on the concept of active image processing

A new generation of scanning electron microscopy (SEM) technology is proposed based on the concept of “active image processing.” In order to collect sufficient data for a purpose which is defined in the utilization of active image processing, we may need more devices from among a variety of useful hardware, for example, a digital scan generator with meaningful parameters and an analog-to-digital converter for ultrahigh density recording. After the data acquisition, the application of some digital image processing techniques is certainly effective, because the method in question is specially designed so that the property of obtained data will be suitable for the application of these techniques. The present technology should produce a variety of attractive options in the field of SEM.     

Highly accurate SNR measurement using the covariance of two SEM images with the identical view

Quality of an SEM image is strongly influenced by the extent of noise. As a well-known method in the field of SEM, the covariance is applied to measure the signal-to-noise ratio (SNR). This method has potential ability for highly accurate measurement of the SNR, which is hardly known until now. If the precautions discussed in this article are adopted, that method can demonstrate its real ability. These precautions are strongly related to "proper acquisition of two images with the identical view," "alignment of an aperture diaphragm," "reduction of charging phenomena," "elimination of particular noises," and "accurate focusing," As necessary, characteristics in SEM signal and noise are investigated from a few standpoints. When using the maximum performance of this measurement, SNR of many SEM images obtained in a variety of the SEM operating conditions and specimens can be measured accurately.     

Reduction in acquisition time of scanning electron microscopy image using complex hysteresis smoothing

Complex hysteresis smoothing (CHS), which was developed for noise removal of scanning electron microscopy (SEM) images some years ago, is utilized in acquisition of an SEM image. When using CHS together, recording time can be reduced without problems by about one-third under the condition of SEM signal with a comparatively high signal-to-noise ratio (SNR). We do not recognize artificiality in a CHS-filtered image, because it has some advantages, that is, no degradation of resolution, only one easily chosen processing parameter (this parameter can be fixed and used in this study), and no processing artifacts. This originates in the fact that its criterion for distinguishing noise depends simply on the amplitude of the SEM signal. The automation of reduction in acquisition time is not difficult, because CHS successfully works for almost all varieties of SEM images with a fairly high SNR.     

Measuring the randomness of micro- and nanostructure spatial distributions: Effects of scanning electron microscope image processing and analysis

The quantitative characterisation of the degree of randomness and aggregation of surface micro- and nanostructures is critical to evaluate their effects on targeted functionalities. To this end, the methods of point pattern analysis (PPA), largely used in ecology and medical imaging, seem to provide a powerful toolset. However, the application of these techniques requires the extraction of the point pattern of nanostructures from their microscope images. In this work, we address the issue of the impact that Scanning Electron Microscope (SEM) image processing may have on the fundamental metric of PPA, that is, the Nearest Neighbour Index (NNI). Using typical SEM images of polymer micro- and nanostructures taken from secondary and backscattered electrons, we report the effects of the (a) noise filtering and (b) binarisation threshold on the value of NNI as well as the impact of the image finite size effects. Based on these results, we draw conclusions for the safe choice of SEM settings to provide accurate measurement of nanostructure randomness through NNI estimation.