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Efficient inductance extraction using circuit-aware techniques

机译:使用电路感知技术的有效电感提取

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Proposes two practical approaches for on-chip inductance extraction to obtain a highly sparsified and accurate inverse inductance matrix K. Both approaches differ from previous methods in that they use circuit characteristics to obtain a sparse, stable and symmetric K, using the concept of resistance-dominant and inductance-dominant lines. Specifically, they begin by finding inductance-dominant lines and forming initial clusters, followed by heuristically enlarging and/or combining these clusters, with the goal of including only the important inductance terms in the sparsified K matrix. Algorithm 1 permits the influence of the magnetic field of aggressor lines to reach the edge of the chip, while Algorithm 2 works under the simplified assumptions that the supply lines have zero ΣjLij(dIj/dt) drops (but have nonzero parasitic Rs and Cs) and that currents cannot return through supply lines beyond a user-defined distance. For reasonable designs, Algorithm 1 delivers a sparsification of 97% for delay and oscillation magnitude errors of 10% and 15%, respectively, as compared to Algorithm 2 where the sparsification can reach 99% for the same delay error.
机译:提出了两种用于片上电感提取的实用方法,以获得高度稀释和精确的逆电感矩阵K.这两种方法都与之前的方法不同,因为它们使用电路特性来使用电阻的概念来获得稀疏,稳定和对称的K,主导和电感 - 主导线。具体而言,它们首先找到电感 - 主导线并形成初始集群,然后通过疏流k矩阵中仅包括所重要的电感术语来形成初始集群,其次是启发式扩大和/或组合这些集群。算法1允许侵略性线磁场的影响到达芯片的边缘,而算法2在电源线具有零Σjlij(dij / dt)滴(但具有非零寄生Rs和Cs)下工作并且这种电流无法通过超出用户定义距离的供应线返回。对于合理的设计,与算法2相比,算法1分别为10%和15%的延迟和振荡幅度误差提供97%的稀疏性,与算法2相同的延迟误差达到99%。

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