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A novel neural-networks parasitic extraction modeling methodology for interconnect parasitic capacitances is developed in rule-based extractors. The current rule-based extractors rely on thousands of parasitic capacit...
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A novel neural-networks parasitic extraction modeling methodology for interconnect parasitic capacitances is developed in rule-based extractors. The current rule-based extractors rely on thousands of parasitic capacitance formulas, each covering few or very limited set of interconnect patterns. These formulas also typically suffer from large errors in corner cases. The proposed methodology provides compact cross-section neural-network models that predict parasitic coupling capacitances for many diverse metal arrangements considering metals connectivity. These models significantly improve the accuracy of rule-based extraction methods. Also, they significantly reduce the pattern mismatches in traditional rule-based methods. The inputs to the proposed compact models are: dimensions of a layout pattern, aggressor polygons, and the required victim polygons for a certain process stack. Two different pattern representations are proposed to be used as inputs to neural-networks models: ratio-based and dimensions-based representations. The proposed methodology shows superior characteristics as compared to traditional existing models in four ways. First, it has high pattern coverage. Second, it mitigates the pattern mismatches. Third, it provides compact, descriptive, and accurate cross-section parasitic models. Fourth, it can handle the increasing accuracy requirements in advanced nodes. The proposed methodology is tested over three test chips of 28nm process node with more than 4.8M interconnect structures. The proposed methodology managed to significantly reduce the pattern mismatches and provided outstanding results as compared to field-solvers with an average error < 0.1% and a standard deviation < 3.2%.
No basic or applied physics research can be done nowadays without the support of computing systems, ranging from cheap personal computers to large multi-user mainframes. Some research fields like high energy physics w...
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ISBN:
(数字)9789814534611
ISBN:
(纸本)9789810216993
No basic or applied physics research can be done nowadays without the support of computing systems, ranging from cheap personal computers to large multi-user mainframes. Some research fields like high energy physics would not exist if computers had not been invented. Departing from the more conventional numerical applications, this series of workshops has been initiated to focus on artificial Intelligence (AI) related developments, such as symbolic manipulation for lengthy and involved algebraic computations, software engineering to assist groups of developers in the design, coding and maintenance of large packages, expert systems to mimic human reasoning and strategy in the diagnosis of equipment or neuralnetworks to implement a model of the brain to solve patternrecognition problems. These techniques, developed some time ago by AI researchers, are confronted by down-to-earth problems arising in high-energy and nuclear physics. All this and more are covered in these proceedings.
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