On Approximate Reduction of Multi-Port Resistor Networks

Ugryumova, MV Maria; Rommes, Joost; Schilders, W.H.A.

doi:10.1007/978-3-642-22453-9_40

Cited by 1 publication

(2 citation statements)

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“…Thus, for a given , resistors satisfying (8) can be deleted. From a practical point of view, estimation (7) is not sharp and does not deliver significant improvements in sparsity neither for original nor for reduced resistor networks [7]. In Section 4, we will derive a different estimation for (6), which is sharp and cheap to compute.…”

Section: Simplification Of Resistor Networkmentioning

confidence: 99%

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Error bounds for reduction of multi‐port resistor networks

Ugryumova

Rommes

Schilders

2013

Int J Numerical Modelling

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SUMMARYThe interconnect layouts of chips can be modeled by large resistor networks. In order to be able to speed up simulations of such large networks, reduction techniques are applied to reduce the size of the networks. For some class of networks, an existing reduction strategy does not provide sufficient reduction in terms of the number of resistors appearing in the final network. In this paper we propose an approach for obtaining a further reduction in the amount of resistors. The suggested approach improves sparsity of the conductance matrix by neglecting resistors which do not contribute significantly to the behavior of the circuit. Explicit error bounds, which give an opportunity to control the errors due to approximation, have been derived. Numerical examples show that the suggested approach appears promising for multi-terminal resistor networks and, in combination with the existing reduction strategy, leads to better reduction.

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Section: Simplification Of Resistor Networkmentioning

confidence: 99%

“…In this section, we suggest an error estimation for the maximum relative error of vector of voltages that shows to be much more sharp than the error estimation based on the condition number (7). In order to derive it, we consider…”

Section: Error Estimation For Jjv Q Vjj Jjvjjmentioning

confidence: 99%