A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images

Nanomaterials, owing to their distinctive features, are crucial across numerous scientific domains, especially in materials science and nanotechnology. Precise segmentation of Scanning Electron Microscope (SEM) images is essential for evaluating attributes such as nanoparticle dimensions, morphology, and distribution. Conventional image segmentation techniques frequently prove insufficient for managing the intricate textures of SEM images, resulting in a laborious and imprecise process. In this research, a modified U-Net architecture is presented to tackle this challenge, utilizing a ResNet50 backbone pre-trained on ImageNet. This model utilizes the robust feature extraction abilities of ResNet50 alongside the effective segmentation performance of U-Net, hence improving both accuracy and computational efficiency in TiO2 nanoparticle segmentation. The suggested model was assessed using performance metrics including accuracy, precision, recall, IoU, and Dice Coefficient. The results indicated a high segmentation accuracy, demonstrated by a Dice score of 0.946 and an IoU of 0.897, with little variability reflected in standard deviations of 0.002071 and 0.003696, respectively, over 200 epochs. The comparison with existing methods demonstrates that the proposed model surpasses previous approaches by attaining enhanced segmentation accuracy. The modified U-Net design serves as an excellent technique for accurate nanoparticle segmentation in SEM images, providing substantial enhancements compared to traditional approaches. This progress indicates the model's potential for wider applications in nanomaterial research and characterization, where precise and efficient segmentation is essential for analysis.

In order to perform nano-manipulation more accurately, it is necessary to study the relationship between the scanning electron microscope (SEM) image and the actual length. In this paper, based on the secondary electron image, the displacement of 1.81nm in SEM was measured the actual displacement was obtained according to the change of the pixels, so as to realize the nanometer displacement calibration and real-time measurement of the nano robot manipulator in SEM. According to the magnification, resolution and scale bar of SEM image, the mapping relationship between pixels and actual length was established. The obvious feature point and area in the SEM image were selected to be marked. The driving signal was applied to the manipulator of the nano robot, and the template matching algorithm was used to track the feature point and area in real time. The change of the feature point in the SEM image was obtained, and the actual displacement of the manipulator was calculated according to the mapping relationship between the pixel points and the actual displacement. We calibrated the displacement of the nanorobots and measured the actual displacement in real time, which laid a foundation for the precise control of the nanorobots in the future.

Image noise is an important factor that influences the accuracy of strain field measurements by means of digital image correlation and scanning electron microscope (SEM) imaging. We propose a new model to quantify the SEM image noise, which extends the classical photon noise model by taking into account the brightness setup in SEM imaging. Furthermore, we apply this model to investigate the impact of different SEM setting parameters on image noise, such as detector, dwell time, spot size, and pressure in the SEM chamber in the context of low vacuum imaging.

Accurate identification and classification of atmospheric particulates can provide the basis for their source apportionment. Most current research studies mainly focus on the classification of atmospheric particles based on the energy spectrum of particles, which has the problems of low accuracy and being time-consuming. It is necessary to study the classification method of atmospheric particles with higher accuracy. In this paper, a convolutional neural network (CNN) model with attention mechanism is proposed to identify and classify the scanning electron microscopy (SEM) images of atmospheric particles. First, this work established a database, Qingdao 2016–2018, for atmospheric particles classification research. This database consists of 3469 SEM images of single particulates. Secondly, by analyzing the morphological characteristics of single particle SEM images, it can be divided into four categories: fibrous particles, flocculent particles, spherical particles, and mineral particles. Thirdly, by introducing attention mechanism into convolutional neural network, an Attention-CNN model for the identification and classification of the four types of atmospheric particles based on the SEM images is established. Finally, the Attention-CNN model is trained and tested based on the SEM images database, and the results of identification and classification for four types of particles are obtained. Under the same SEM images database, the classification results from Attention-CNN are compared with those of CNN and SVM. It is found that Attention-CNN has higher classification accuracy and reduces significantly the misclassification number of particles, which shows the focusing effect of attention mechanism.

The MeX™ software is a useful tool for tridimensional data collection for surface evaluation and could be relevant to evaluate the same specimen in different phases of the study, assuming repeated measures of dental enamel roughness. The aim of this study was to evaluate the influence of sample metallization for dental enamel roughness analysis with 3D images reconstructed using MeX™ software from Scanning Electron Microscopy (SEM) images. The influence of 74.98% (%mol/mol) argon-oxygen plasma for carbon layer removal on surface roughness of the metallized specimen was also evaluated. Dental enamel specimens were prepared for SEM analysis with and without carbon metallization using conventional or environmental modes. Argon-oxygen plasma for carbon layer removal was used and surface roughness was re-evaluated. Roughness obtained by SEM and MeX™ reconstructed images, with or without metallization, did not differ. No significant alteration on surface roughness after carbon layer removal using plasma was found. SEM baseline evaluation using conventional mode without sample preparation and in environmental mode were not comparable. Roughness of enamel 3D images reconstructed with MeX™ software from SEM images, with or without metallization was similar. The 74.98% (%mol/mol) argon-oxygen plasma removed the carbon layer with no effect on enamel roughness.

Boosting Electrocatalytic CO ₂ Reduction with Conjugated Bimetallic Co/Zn Polyphthalocyanine Frameworks

Nanocomposites, where nanoscale inclusions are embedded in a matrix material, have attracted increasing research attention in recent years. Here, we limit our focus to polymer nanocomposites. Nano-reinforcements (for instance, nanoparticles, nanowires, and nanotubes) have largely disappointed us in manufacturing lightweight, high-strength, and high-toughness polymer composites because of their agglomeration in the matrix—one of the major drawbacks limiting their reinforcing effect. Dispersion of nano-reinforcements is not trivial and often requires expensive facilities and/or processing procedures. It has proven difficult to disperse nano-reinforcements in the matrix via conventional chemical, mechanical, and physical methods. To date, a perfect dispersion of nanoreinforcements in a matrix material has not been achieved. Despite considerable efforts, the mechanical properties of polymer nanocomposites are still far below their theoretically predicted potential. On the other hand, nano-reinforcements and their composites produced through the currently available methods are usually too expensive for use in large-scale industrial applications. To solve these two critical problems, we turned our attention to investigate new concepts for nano-reinforcement dispersion and new synthesis techniques that enable low-cost, high-throughput manufacturing of nanocomposites. Boron carbide (B4C), a lightweight refractory semiconductor, is the third hardest material known to man at room temperature and becomes the hardest above 1100 8C. It has many unique properties such as low density (2.5 g cm ), a small thermal extension coefficient (5.73 10 6 K ), a high melting point (>2400 8C), high resistance to chemical attacks, high thermal stability, a high Seebeck coefficient, and a large neutron absorption cross-section. The combination of these superior properties gives rise to numerous applications, especially under extreme conditions, for example, lightweight body armor, aircraft armor, abrasive wear-resistant materials, solid-state neutron detectors, and, potentially, power generation in deep-space flight applications. Recently, 1D B4C nanostructures, including nanowires and nanorods, have attracted significant attention. To synthesize B4C nanowires, the carbon source is essential. Among most of the available synthesis methods for 1D B4C nanostructures, artificial materials such as carbon black, carbon nanotubes, and activate carbon are selected as carbon source. A stepwise synthetic procedure was usually adopted: metal-catalyst nanoparticles were first synthesized and subsequently added to the precursors as the catalyst. Cotton has been used by mankind for at least 7000 years. It is the oldest and most commonly used nanoporous material, which is constructed from polysaccharide chains arranged into amorphous and crystalline regions. Today, the world production of cotton is approximately 20 million tons per annum, mainly for clothing, paper, and medical uses. In this work, a commercial cotton T-shirt was used as both the template and the carbon source to synthesize large quantities of radially aligned B4C nanowires on carbon microfibers. The as-synthesized B4C-nanowire/carbon-microfiber hybrid structures were then simply dipped in an epoxy resin bath to achieve a high dispersion of B4C nanowires in the matrix and highthroughput manufacturing of laminated polymer nanocomposites. A digital camera image of the 100% cotton T-shirt is shown in Figure 1a. A striking and extremely useful feature of cotton is its ability of absorbing large quantities of liquids, particularly water. Figure 1b is a representative digital camera image of a piece of the cotton T-shirt after the absorption of a solution with Ni(NO3)2 6H2O and amorphous boron powders. Figure 1c shows 6 pieces of final textiles. A typical scanning electron microscopy (SEM) image of carbon microfibers covered with B4C nanowires is shown in Figure 1d. The B4C nanowires, with a diameter ranging from 80 to 200 nm and a length greater than 4mm, grew radially on the entire length of the carbon microfiber (Fig. 1e). The SEM image (Fig. 1f) and transmission electron microscopy (TEM) image (Fig. 1g) together reveal a catalyst particle on the tip of each nanowire. Most of the catalyst particles were spherical and had a diameter distribution of 90 to 250 nm. TEM image (Fig. 1g) also shows that the diameter of the catalyst is larger than that of the corresponding B4C nanowire. Energy dispersive spectroscopy (EDS) reveals that the catalyst particle on the nanowire tip is nickel boride. This suggests that a catalyst is essential for growing such B4C nanowires. Our experimental results proved that most of the catalyst particles can be easily removed by acid treatment (HNO3þHF or HNO3). The B4C

A methodology for the automated analysis of metal powder scanning electron microscope (SEM) images towards material characterization is developed and presented. This software-based tool takes advantage of a combination of recent artificial intelligence advances (mask R-CNN), conventional image processing techniques, and SEM characterization domain knowledge to assess metal powder quality for additive manufacturing applications. SEM is being used for characterizing metal powder alloys, specifically by quantifying the diameter and number of spherical particles, which are key characteristics for assessing the quality of the analyzed powder. Usually, SEM images are manually analyzed using third-party analysis software, which can be time-consuming and often introduces user bias into the measurements. In addition, only a few non-statistically significant samples are taken into consideration for the material characterization. Thus, a method that can overcome the above challenges utilizing state-of-the-art instance segmentation models is introduced. The final proposed model achieved a total mask average precision (mAP50) 67.2 at an intersection over union of 0.5 and with prediction confidence threshold of 0.4. Finally, the predicted instance masks are further used to provide a statistical analysis that includes important metrics such as the particle size distinction.

Effective decapsulation of copper wire-bonded microelectronic devices for reliability assessment

This is the first of a series of papers on the theme of automated pollen analysis. The automation of pollen analysis could result in numerous advantages for the reconstruction of past environments, with larger data sets made practical, objectivity and fine resolution sampling. There are also applications in apiculture and medicine. Previous work on the classification of pollen using texture measures has been successful with small numbers of pollen taxa. However, as the number of pollen taxa to be identified increases, more features may be required to achieve a successful classification. This paper describes the use of simple geometric measures to augment the texture measures. The feasibility of this new approach is tested using scanning electron microscope (SEM) images of 12 taxa of fresh pollen taken from reference material collected on Henderson Island, Polynesia. Pollen images were captured directly from a SEM connected to a PC. A threshold grey‐level was set and binary images were then generated. Pollen edges were then located and the boundaries were traced using a chain coding system. A number of simple geometric variables were calculated directly from the chain code of the pollen and a variable selection procedure was used to choose the optimal subset to be used for classification. The efficiency of these variables was tested using a leave‐one‐out classification procedure. The system successfully split the original 12 taxa sample into five sub‐samples containing no more than six pollen taxa each. The further subdivision of echinate pollen types was then attempted with a subset of four pollen taxa. A set of difference codes was constructed for a range of displacements along the chain code. From these difference codes probability variables were calculated. A variable selection procedure was again used to choose the optimal subset of probabilities that may be used for classification. The efficiency of these variables was again tested using a leave‐one‐out classification procedure. The proportion of correctly classified pollen ranged from 81% to 100% depending on the subset of variables used. The best set of variables had an overall classification rate averaging at about 95%. This is comparable with the classification rates from the earlier texture analysis work for other types of pollen. Copyright © 2004 John Wiley & Sons, Ltd.

Boosting the Rate Performance of Li–S Batteries via Highly Dispersed Cobalt Nanoparticles Embedded into Nitrogen-Doped Hierarchical Porous Carbon

In this paper, we propose an accurate automatic defect detection method using the quadtree decomposition on scanning electron microscope (SEM) images. The proposed method consists of five steps including alignment between the inspection and reference images, edge relaxation, defect candidate detection, multichannel integration, and defect scoring. Since the periodicity of semiconductor SEM images is usually high, alignment can be failed due to a lot of local minima. To resolve this problem, we propose the enhancement method of non-periodic areas in SEM images. Then, we propose an edge relaxation technique to minimize the falsely remained edges in the aligned and subtracted image due to different focusing and exposure between two images. Then, the defect candidates are detected by analyzing the inhomogeneity based on quadtree decomposition, which is enhanced by our outlier-robust decomposition rule and adaptive decomposition thresholding to prevent over/under decomposition. Whereas the conventional defect detection system focuses only on the intensity of defects, our method considers both the inhomogeneity and intensity of defects. Field test results demonstrated that the proposed method provided more accurate defect detection with faster speed as compared to the conventional system. The proposed method can much reduce the automatic defect review rework compared with the conventional system.

The advent of small-scale testing procedures coupled with scanning electron microscopy (SEM) imaging allow for high-resolution digital image correlation (DIC) studies to examine strain localization at the grain size length scale. A systematic study was performed to determine how speckle patterning parameters (speckle density and shape) affect strain resolution of DIC using SEM imaging. Strain resolution increased with increased speckle density from 23 to 58 % area fraction. Patterns with less than 23 % area fraction exhibited significant signal noise, and a loss in strain resolution due to inadequate correlation. It was also observed that when the edges of square speckles were aligned with SEM rastering directions, the noise in the e yy data was double the noise in the e xx data. Rotating the speckle to eliminate edge alignment with the rastering direction significantly decreased the e yy strain noise. Competing optimization requirements for the correlation parameters were needed to minimize strain intensity noise or maximize spatial resolution. Application of the optimization techniques to high temperature in-situ studies of Ni-based superalloys will also be presented.

As the technology node shrinks, the systematic defects such as the missing and extra pattern are generated in the process of cell patterning. The defect such as the extra and missing pattern can lead to a critical failure on device. Subsequently, the systematic defect in the UBE could be an etch-gas path on proceeding the etch process and cause the quality degradation of device. In this paper, we propose an automated system to inspect the systematic defect in the cell array with the scanning electron microscope (SEM) image and the physical design. The method consists of conversion, matching, and detection. In the conversion method, the SEM image is converted into the layout using image processing such as noise reduction, segmentation and contour tracing. In the following matching method, gradient descent optimization is used to match the coordinate of layout converted from the SEM image with the coordinate of physical design. In the final detection method, the defect is detected by inspecting the patterns of two layouts. On the test of 2,500 data, we confirmed that the accuracy improved from 97% to 100% as a result of comparing the engineer's visual inspection method with the proposed method. In addition, the turnaround time (TAT) is improved by approximately 40 times. The proposed system is currently applied to DRAM products and used for the field of design for manufacturing (DFM) and manufacturing process.

Evaluating the ability of energy dispersive X-ray analysis to monitor binding oil content of carbon paste electrodes exposed to biofouling agents