Automatic Focus Algorithm for IC Wafer Image Sampling by Adaptive Lifting Scheme Denoising

Deng Yaohua, L. Guixiong, Wu Liming, Zhang Yingmin, W. Guitang
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引用次数: 1

Abstract

High SNR (signal to noise ratio) image is deeply needed in the precise analysis of IC wafer micro-image; current denoising algorithms cant reach the analytic precision in some level. In tins paper, one image denoising algorithms is putted forward based on adaptive lifting scheme, the construction of Haar wavelet and CDF (2,2) is given, the signal is decomposed by wavelet base Haar or wavelet base CDF (2,2) adaptively along four directions (horizon, verticality, 45 degree and 135 degree) in the step of predicting, the wavelet coefficients are calculated separately at each direction, all the thresholds are gained using wavelet soft-thresholding principle, the optimal thresholds minimize the error of the result as compared to these the signal is decomposed along horizon and verticality. Finally the definition of the image is appraised with gray gradient judging function, the experimental data shows that the focus error is no more 4 um, the display definition of the image is improved.
基于自适应提升降噪的IC晶圆图像自动聚焦算法
精确分析IC晶圆微图像需要高信噪比的图像;现有的去噪算法在一定程度上无法达到解析精度。本文提出了一种基于自适应提升方案的图像去噪算法,给出了Haar小波和CDF(2,2)的构造,在预测步骤中对信号进行小波基Haar或小波基CDF(2,2)沿水平、垂直、45度和135度四个方向的自适应分解,在每个方向分别计算小波系数,利用小波软阈值原理获得所有阈值。最佳阈值使结果的误差最小,与此相比,信号沿水平和垂直方向分解。最后利用灰度梯度判断函数对图像的清晰度进行评价,实验数据表明,聚焦误差不超过4 μ m,提高了图像的显示清晰度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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