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Influence of roughness on the detection of mechanical characteristics of low-k film by the surface acoustic waves
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作者 肖夏 陶冶 孙远 《Chinese Physics B》 SCIE EI CAS CSCD 2014年第10期424-428,共5页
The surface acoustic wave (SAW) technique is a precise and nondestructive method to detect the mechanical charac- teristics of the thin low dielectric constant (low-k) film by matching the theoretical dispersion c... The surface acoustic wave (SAW) technique is a precise and nondestructive method to detect the mechanical charac- teristics of the thin low dielectric constant (low-k) film by matching the theoretical dispersion curve with the experimental dispersion curve. In this paper, the influence of sample roughness on the precision of SAW mechanical detection is inves- tigated in detail. Random roughness values at the surface of low-k film and at the interface between this low-k film and the substrate are obtained by the Monte Carlo method. The dispersive characteristic of SAW on the layered structure with rough surface and rough interface is modeled by numerical simulation of finite element method. The Young's moduli of the Black DiamondTM samples with different roughness values are determined by SAWs in the experiment. The results show that the influence of sample roughness is very small when the root-mean-square (RMS) of roughness is smaller than 50 nm and correlation length is smaller than 20 μm. This study indicates that the SAW technique is reliable and precise in the nondestructive mechanical detection for low-k films. 展开更多
关键词 low-k film mechanical character detection rough surface rough interface surface acoustic wave
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Handwritten billet number recognition algorithm based on edge extraction
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作者 ZONG Dexiang SHI Guifen HE Yonghui 《Baosteel Technical Research》 CAS 2021年第3期22-27,共6页
Character recognition has always been a hot topic in the field of computer vision.However,it is often difficult to obtain high-precision results in the actual scene owing to factors such as lighting conditions and ima... Character recognition has always been a hot topic in the field of computer vision.However,it is often difficult to obtain high-precision results in the actual scene owing to factors such as lighting conditions and imaging angle.Aiming at the problem of handwritten billet identification in the steel industry,this paper proposes the use of the canny edge extraction method to enhance the contour characteristics of characters.This technique is combined with the object detection network to achieve the automatic identification of blank square numbers and solve the problem of automatic tracking of billet logistics in the production process.The proposed algorithm is applied to the site with more than 2019 images containing characters in the test set.Results show that the proposed algorithm has good practical application potential. 展开更多
关键词 billet character recognition character rotation mechanism canny operator deep learning network
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