scholarly journals Simultaneous Interface Defect Density and Differential Capacitance Imaging by Time-Resolved Scanning Nonlinear Dielectric Microscopy

2021 ◽  
Author(s):  
Kohei Yamasue ◽  
Yasuo Cho
Keyword(s):  
Defect Density ◽  
Differential Capacitance ◽  
Time Resolved ◽  
Nonlinear Dielectric ◽  
Voltage Sweep ◽  
Difference Image ◽  
Cross Correlation Analysis ◽  
Interface Defect ◽  
The Difference ◽  
Analysis Of The Images

Abstract We investigate non-uniformity at SiO2/SiC interfaces by time-resolved scanning nonlinear dielectric microscopy, which permits the simultaneous nanoscale imaging of interface defect density (Dit) and differential capacitance (dC/dV) at insulator-semiconductor interfaces. Here we perform the cross correlation analysis of the images with spatially non-uniform clustering distributions reported previously. We show that Dit images are not correlated with the simultaneous dC/dV images significantly but with the difference image between the two dC/dV images taken with different voltage sweep directions. The results indicate that the dC/dV images visualize the non-uniformity of the total interface charge density and the difference images reflect that of Dit at a particular energy range.

Radio Frequency Power Dependence of Defect Density at Hydrogenated Amorphous Silicon Interface

1991 ◽  
Vol 237 ◽  
Author(s):  
Hisanori Ihara ◽  
Takeo Sakakubo ◽  
Hidetoshi Nozaki
Keyword(s):  
Radio Frequency ◽  
Amorphous Silicon ◽  
Buffer Layer ◽  
Defect Density ◽  
Hydrogenated Amorphous Silicon ◽  
Rf Power ◽  
Interface Defect ◽  
The Difference ◽  
Silicon Interface ◽  
Hydrogenated Amorphous

ABSTRACTA hydrogenated amorphous silicon (a-Si:H) interface fabrication technology for the plasma CVD method, which can produce low interface defect density, is presented. The relation between the interface defect density and radio frequency (RF) power was investigated. As a result, the difference between the interface defect density and the bulk defect density decreased with increasing the RF power. A high RF power (25 W) a-Si:H buffer layer 5 nm thick was deposited on the interface before depositing low RF power (5 W) a-Si:H layer with a low bulk defect density. It has been found that the ideal defect density distribution, which shows the uniform distribution with the very low defect density (4.2×1014 cm) from the i/i interface to the bulk, can be accomplished by 5 nm buffer layer.

scholarly journals A diffusion anisotropy descriptor links morphology effects of H-ZSM-5 zeolites to their catalytic cracking performance

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Xiaoliang Liu ◽  
Jing Shi ◽  
Guang Yang ◽  
Jian Zhou ◽  
Chuanming Wang ◽  
...  
Keyword(s):  
Catalytic Activity ◽  
Catalytic Cracking ◽  
Catalytic Performance ◽  
Zeolite Catalysts ◽  
Time Resolved ◽  
Cracking Performance ◽  
Morphology Effect ◽  
Ft Ir ◽  
The Difference ◽  
Dynamics Simulations

AbstractZeolite morphology is crucial in determining their catalytic activity, selectivity and stability, but quantitative descriptors of such a morphology effect are challenging to define. Here we introduce a descriptor that accounts for the morphology effect in the catalytic performances of H-ZSM-5 zeolite for C4 olefin catalytic cracking. A series of H-ZSM-5 zeolites with similar sheet-like morphology but different c-axis lengths were synthesized. We found that the catalytic activity and stability is improved in samples with longer c-axis. Combining time-resolved in-situ FT-IR spectroscopy with molecular dynamics simulations, we show that the difference in catalytic performance can be attributed to the anisotropy of the intracrystalline diffusive propensity of the olefins in different channels. Our descriptor offers mechanistic insight for the design of highly effective zeolite catalysts for olefin cracking.

scholarly journals Overheating In Silicon During Pulsed-Laser Irradiation?

1985 ◽  
Vol 51 ◽  
Author(s):  
B. C. Larson ◽  
J. Z. Tischler ◽  
D. M. Mills
Keyword(s):  
Laser Irradiation ◽  
Thermal Strain ◽  
Pulsed Laser ◽  
Interface Velocity ◽  
High Energy ◽  
Time Resolved ◽  
Synchrotron Source ◽  
Zero Velocity ◽  
The Difference ◽  
Pulsed Laser Irradiation

ABSTRACTNanosecond resolution time-resolved x-ray diffraction measurements of thermal strain have been used to measure the interface temperatures in silicon during pulsed-laser irradiation. The pulsed-time-structure of the Cornell High Energy Synchrotron Source (CHESS) was used to measure the temperature of the liquid-solid interface of <111> silicon during melting with an interface velocity of 11 m/s, at a time of near zero velocity, and at a regrowth velocity of 6 m/s. The results of these measurements indicate 110 K difference between the temperature of the interface during melting and regrowth, and the measurement at zero velocity shows that most of the difference is associated with undercooling during the regrowth phase.

scholarly journals A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1746
Author(s):  
Salman Ahmadi ◽  
Saeid Homayouni
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Active Contours ◽  
Training Data ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Difference Image ◽  
Proposed Model ◽  
The Difference ◽  
Aperture Radar

In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods.

The Design of Real-Time Face Detection System Based on FPGA

2013 ◽  
Vol 473 ◽  
pp. 231-234
Author(s):  
Su Hua Chen ◽  
Xu Fang ◽  
Yong Guang Liu ◽  
Jun Wang
Keyword(s):  
Processing Speed ◽  
Detection System ◽  
Main Body ◽  
Infrared Source ◽  
Difference Image ◽  
Small Influence ◽  
The Face ◽  
The Difference ◽  
Locating System ◽  
Image Difference

The design attempts for thefirst time to realize face locating system on the FPGA platform using themethod combined initiative infrared source with image difference. Through imagedifference process, the system obtains a difference image without backgroundinterference which takes the face as the main body. It can obtain the personface boundary by projecting the difference image in the horizontal and verticaldirection. The system processing speed amount s to the video source frequency25 frame per second, satisfying the timely request; the method of initiativeinfrared source makes the exterior have small influence on the image andguarantees the robustness of the system.

scholarly journals SAR Image Change Detection Based on Multi-scale Feature Extraction

2021 ◽  
Vol 10 (4) ◽  
pp. 077-081
Author(s):  
Xiaoqian Yuan ◽  
Chao Chen ◽  
Shan Tian ◽  
Jiandan Zhong
Keyword(s):  
Feature Extraction ◽  
Change Detection ◽  
Speckle Noise ◽  
Kernel Matrix ◽  
Sar Image ◽  
Scale Feature ◽  
Difference Image ◽  
Multi Scale ◽  
Image Change Detection ◽  
The Difference

In order to improve the contrast of the difference image and reduce the interference of the speckle noise in the synthetic aperture radar (SAR) image, this paper proposes a SAR image change detection algorithm based on multi-scale feature extraction. In this paper, a kernel matrix with weights is used to extract features of two original images, and then the logarithmic ratio method is used to obtain the difference images of two images, and the change area of the images are extracted. Then, the different sizes of kernel matrix are used to extract the abstract features of different scales of the difference image. This operation can make the difference image have a higher contrast. Finally, the cumulative weighted average is obtained to obtain the final difference image, which can further suppress the speckle noise in the image.

scholarly journals A simple approach for monitoring vegetation change using time series remote sensing analysis: A case study from the Thathe Vondo Area in Limpopo Province, South Africa

2021 ◽  
Vol 117 (7/8) ◽  
Author(s):  
Nndanduleni Muavhi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Simple Approach ◽  
Limpopo Province ◽  
Difference Image ◽  
Normalised Difference Vegetation Index ◽  
Gain And Loss ◽  
The Difference

This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.

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