A novel technique based on the statistical autoregressive (AR) model has recently been developed as a solution to estimate the signal-to-noise ratio (SNR) in scanning electron microscope (SEM) images. In another research study, the authors also developed an algorithm by cascading the AR model with the Lagrange time delay (LTD) estimator. This technique is named the mixed Lagrange time delay estimation autoregressive (MLTDEAR) model. In this paper, the fundamental performance limits for the problem of single-image SNR estimation as derived from the Cramer-Rao inequality is presented. We compared the experimental performances of several existing methods--the simple method, the first-order linear interpolator, the AR-based estimator as well as the MLTDEAR method--with respect to this performance bound. In a few test cases involving different images, the efficiency of the MLTDEAR single-image estimation technique proved to be significantly better than that of the other three methods. Study of the effect of different SEM setting conditions that affect the autocorrelation function curve is also discussed.
A new technique to quantify signal-to-noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson-Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first-order linear interpolation and nearest neighbourhood combined with first-order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation.
The quality of an image generated by a scanning electron microscope is dependent on secondary emission, which is a strong function of surface condition. Thus, empirical formulae and available databases are unable to take into account actual metrology conditions. This paper introduces a simple and reliable measurement technique to measure secondary electron yield (delta) and backscattered electron yield (eta) that is suitable for in-situ measurements on a specimen immediately prior to imaging. The reliability of this technique is validated on a number of homogenous surfaces. The measured electron yields are shown to be within the range of published data and the calculated signal-to-noise ratio compares favourably with that estimated from the image.
A new technique based on cubic spline interpolation with Savitzky-Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real-time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky-Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal-to-noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation-based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.
A new technique based on cubic spline interpolation with Savitzky-Golay noise reduction filtering is designed to estimate signal-to-noise ratio of scanning electron microscopy (SEM) images. This approach is found to present better result when compared with two existing techniques: nearest neighbourhood and first-order interpolation. When applied to evaluate the quality of SEM images, noise can be eliminated efficiently with optimal choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.
An improvement to the previously proposed Canny optimization technique for scanning electron microscope image colorization is reported. The additional process is adaptive tuning, where colour tuning is performed adaptively, based on comparing the original luminance values with calculated luminance values. The complete adaptive Canny optimization technique gives significantly better mechanical contrast on scanning electron microscope grey-scale images than do existing methods.
Interpolation techniques that are used for image magnification to obtain more useful details of the surface such as morphology and mechanical contrast usually rely on the signal information distributed around edges and areas of sharp changes and these signal information can also be used to predict missing details from the sample image. However, many of these interpolation methods tend to smooth or blur out image details around the edges. In the present study, a Lagrange time delay estimation interpolator method is proposed and this method only requires a small filter order and has no noticeable estimation bias. Comparing results with the original scanning electron microscope magnification and results of various other interpolation methods, the Lagrange time delay estimation interpolator is found to be more efficient, more robust and easier to execute.
A new and robust parameter estimation technique, named Gaussian-Taylor interpolation, is proposed to predict the signal-to-noise ratio (SNR) of scanning electron microscope images. The results of SNR and variance estimation values are tested and compared with piecewise cubic Hermite interpolation, quadratic spline interpolation, autoregressive moving average and moving average. Overall, the proposed estimations for noise-free peak and SNR are most consistent and accurate to within a certain acceptable degree compared with the others.
A new filter is developed for the enhancement of scanning electron microscope (SEM) images. A mixed Lagrange time delay estimation auto-regression (MLTDEAR)-based interpolator is used to provide an estimate of noise variance to a standard Wiener filter. A variety of images are captured and the performance of the filter is shown to surpass the conventional noise filters. As all the information required for processing is extracted from a single image, this method is not constrained by image registration requirements and thus can be applied in real-time in cases where specimen drift is presented in the SEM image.
Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two-dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub-band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub-band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.
A number of techniques have been proposed during the last three decades for noise variance and signal-to-noise ratio (SNR) estimation in digital images. While some methods have shown reliability and accuracy in SNR and noise variance estimations, other methods are dependent on the nature of the images and perform well on a limited number of image types. In this article, we prove the accuracy and the efficiency of the image noise cross-correlation estimation model, vs. other existing estimators, when applied to different types of scanning electron microscope images.
This article focuses on the localization of burn mark in MOSFET and the scanning electron microscope (SEM) inspection on the defect location. When a suspect abnormal topography is shown on the die surface, further methods to pin-point the defect location is necessary. Fault localization analysis becomes important because an abnormal spot on the chip surface may and may not have a defect underneath it. The chip surface topography can change due to the catastrophic damage occurred at layers under the chip surface, but it could also be due to inconsistency during metal deposition in the wafer fabrication process. Two localization techniques, liquid crystal thermography and emission microscopy, were performed to confirm that the abnormal topography spot is the actual defect location. The tiny burn mark was surfaced by performing a surface decoration at the defect location using hot hydrochloric acid. SEM imaging, which has the high magnification and three-dimensional capabilities, was used to capture the images of the burn mark.
A new and robust parameter estimation technique, named image noise cross-correlation, is proposed to predict the signal-to-noise ratio (SNR) of scanning electron microscope images. The results of SNR and variance estimation values are tested and compared with nearest neighborhood and first-order interpolation. Overall, the proposed method is best as its estimations for the noise-free peak and SNR are most consistent and accurate to within a certain acceptable degree, compared with the others.
By applying a hexagon-diamond search (HDS) method to an ultrasound image, the path of an object is able to be monitored by extracting images into macro-blocks, thereby achieving image redundancy is reduced from one frame to another, and also ascertaining the motion vector within the parameters searched. The HDS algorithm uses six search points to form the six sides of the hexagon pattern, a centre point, and a further four search points to create diamond pattern within the hexagon that clarifies the focus of the subject area.
Images of scanning electron microscope are usually in the monochrome mode. A simple and user-friendly approach is proposed to improve the mechanical contrast of the scanning electron microscope grey images. Also, most colourization techniques involve image segmentation or region tracking, which tend to degrade the image with fuzzy or complex region boundaries. A technique is proposed, which is a hybrid between the Canny edge detection technique and the optimization technique. Compared with existing methods, the new Canny optimization technique gives satisfactory results for scanning electron microscope images.
Detection of cracks from stainless steel pipe images is done using contrast stretching technique. The technique is based on an image filter technique through mathematical morphology that can expose the cracks. The cracks are highlighted and noise removal is done efficiently while still retaining the edges. An automated crack detection system with a camera platform has been successfully implemented. We compare crack extraction in terms of quality measures with those of Otsu's threshold technique and the another technique (Iyer and Sinha, 2005). The algorithm shown is able to achieve good results and perform better than these other techniques.
To reduce undesirable charging effects in scanning electron microscope images, Rayleigh contrast stretching is developed and employed. First, re-scaling is performed on the input image histograms with Rayleigh algorithm. Then, contrast stretching or contrast adjustment is implemented to improve the images while reducing the contrast charging artifacts. This technique has been compared to some existing histogram equalization (HE) extension techniques: recursive sub-image HE, contrast stretching dynamic HE, multipeak HE and recursive mean separate HE. Other post processing methods, such as wavelet approach, spatial filtering, and exponential contrast stretching, are compared as well. Overall, the proposed method produces better image compensation in reducing charging artifacts.
Whole-genome sequencing across multiple samples in a population provides an unprecedented opportunity for comprehensively characterizing the polymorphic variants in the population. Although the 1000 Genomes Project (1KGP) has offered brief insights into the value of population-level sequencing, the low coverage has compromised the ability to confidently detect rare and low-frequency variants. In addition, the composition of populations in the 1KGP is not complete, despite the fact that the study design has been extended to more than 2,500 samples from more than 20 population groups. The Malays are one of the Austronesian groups predominantly present in Southeast Asia and Oceania, and the Singapore Sequencing Malay Project (SSMP) aims to perform deep whole-genome sequencing of 100 healthy Malays. By sequencing at a minimum of 30× coverage, we have illustrated the higher sensitivity at detecting low-frequency and rare variants and the ability to investigate the presence of hotspots of functional mutations. Compared to the low-pass sequencing in the 1KGP, the deeper coverage allows more functional variants to be identified for each person. A comparison of the fidelity of genotype imputation of Malays indicated that a population-specific reference panel, such as the SSMP, outperforms a cosmopolitan panel with larger number of individuals for common SNPs. For lower-frequency (<5%) markers, a larger number of individuals might have to be whole-genome sequenced so that the accuracy currently afforded by the 1KGP can be achieved. The SSMP data are expected to be the benchmark for evaluating the value of deep population-level sequencing versus low-pass sequencing, especially in populations that are poorly represented in population-genetics studies.