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Improved critical area prediction by application of pattern recognition techniques

机译:通过模式识别技术的应用改进了关键区域的预测

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A novel technique to predict the susceptibility of VLSIC layouts to particle-induced failure is presented. The investigation applies the concept of Critical Areas to determine the failure probability of rectilinear layouts. A unique methodology has been devised, with which the relationship between the recurrent patterns observed within the topology of a layout and the robustness of the design against particulate contamination can be quantified. The critical area is formulated as a function of the dimensions of the identifiable geometrical shapes and the characteristics of the defect model. In comparison with current approximation methods, the proposed technique is quick, exact, adaptable for different defect models and guarantees computational accuracy irrespective of layout complexity.
机译:提出了一种预测VLSIC布局对粒子引起的故障的敏感性的新技术。该调查采用临界区域的概念来确定直线布局的失效概率。已经设计出一种独特的方法,利用该方法可以量化在布局的拓扑结构中观察到的循环模式与设计对颗粒污染的鲁棒性之间的关系。根据可识别的几何形状的尺寸和缺陷模型的特征来制定关键区域。与目前的近似方法相比,所提出的技术快速,准确,适用于不同的缺陷模型,并且无论布局复杂程度如何,都能保证计算精度。

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