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Canonical Symbolic Analysis of Large Analog Circuits with Determinant Decision Diagrams
 IEEE TRANS. ON COMPUTERAIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
, 2000
"... Symbolic analogcircuit analysis has many applications, and is especially useful for analog synthesis and testability analysis. Existing approaches rely on two forms of symbolic expression representation: expanded sumofproduct form or arbitrarily nested form. Expanded form suffers the problem that ..."
Abstract

Cited by 23 (8 self)
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Symbolic analogcircuit analysis has many applications, and is especially useful for analog synthesis and testability analysis. Existing approaches rely on two forms of symbolic expression representation: expanded sumofproduct form or arbitrarily nested form. Expanded form suffers the problem that the number of product terms grows exponentially with the size of a circuit, and approximation has to be used. Nested form is not canonical, i.e., many representations exist for a symbolic expression, and manipulations with the nested form are often complicated. In this paper, we present a new approach to exact and canonical symbolic analysis by exploiting the sparsity and sharing of product terms. It consists of representing the symbolic determinant of a circuit matrix by a graphcalled determinant decision diagram (DDD)and performing symbolic analysis by graph manipulations. We showed that DDD construction, as well as many symbolic analysis algorithms, can be performed in time complex...
Symbolic Analysis of Power/Ground Networks Using MomentMatching Methods
"... This paper presents an eventdriven algorithm for the symbolic analysis of power and ground bus networks using moment matching techniques to estimate the transfer function at each node in the P#G network. The P#G network is modeled by a hierarchical combination of mesh and tree structures that are c ..."
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This paper presents an eventdriven algorithm for the symbolic analysis of power and ground bus networks using moment matching techniques to estimate the transfer function at each node in the P#G network. The P#G network is modeled by a hierarchical combination of mesh and tree structures that are composed of a collection of RC# segments and pulldown #or pullup# switches. The switches are symbolically represented by Boolean variables and a compiled symbolic code is generated only once for each P#G network. The transientwaveforms are then produced by repetitiveevaluation of the symbolic output. The results show that the symbolic implementation is an order of magnitude faster, with reasonably good accuracy, than using a traditional analog circuit simulator like SPICE.