In the paper, the effect of Ge profiles on the performance of lateral SiGe heterojunction bipolar transistor (LHBT) on silicon-on-insulator (SOI) substrate with positive bias voltage has been studied. With the aid of substrate bias (VS), the peak current gain (β) of LHBT with trapezoidal Ge profile is obviously enhanced, which is almost as same as LHBT with uniform Ge profile. At the same time, the β of LHBT with trapezoidal Ge profile is kept as temperature increases. However, the β of LHBT with uniform Ge profile is decreased as temperature increases. Furthermore, for LHBT with trapezoidal Ge profile, the peak junction temperature is lowered by 20.51K and the cut-off frequency (fT) is also improved by 158.4 GHz when compared with that of the uniform one. The results show that LHBTs with trapezoidal Ge profile could achieve the superior electrical, thermal, and high frequency performance at the same time, which provides useful guidelines to design SiGe HBTs for microwave and digital or mixed-signal applications.
The influence of an evanescent field formed by two evanescent waves under the total internal reflection on the dynamics of motion of separate erythrocyte into blood plasma is demonstrated. Computer simulation of red blood cell motion into evanescent field and experimental demonstration of rotational and rectilinear motion expand the possibilities of using optical evanescent waves in applied tasks of nanophysics and biomedicine. The vertical spin produced by the illumination of a cell by the linearly polarized wave with the azimuth of polarization 45º demonstrates unique ability to control transverse motion of the nanoobject that is not characterized to the action of spin momentum inherent to the classical circular polarized optical beam.
A new image segmentation method based on Markov Random Field (MRF) and Two-Dimensional Histogram Method of
Fuzzy Clustering as well as Dempster-Shafer (D-S) evidence theory is presented in this paper.The application of Markov
Random Field to image restoration and segmentation can effectively remove noise and get more accurate segmentation
results; And the application of Fuzzy Clustering Theory together with Two-Dimensional Histogram image segmentation
methods can get more satisfactory segmentation results; However, these two ways leads to different classification results
while classifying the controversial pixels in images, so we can use the Dempster-Shafer evidence theory to assign the
controversial points to the plausibility interval, and then divide them. This paper will adopt the above three theories to
propose a human brain image segmentation research method. Experimental result shows that the method solves the
problem of the class attribution of the controversial points, and the segmentation result is more in line with human vision.
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