Ansys, Inc.

Helic, Inc.

Marios Visvardis

Efthymios Efstathiou

Stefanos Stefanou

Errikos Lourandakis

Periklis Liaskovitis

Dimitrios Skrepetos

A CAD method for capacitance extraction includes decomposing a semiconductor structure model representing a three-dimensional, into virtual layers. Each virtual layer has polygons corresponding to conductors in the semiconductor structure model. The semiconductor structure model or includes a geometric point. The method includes creating spatial indexes respectively for the virtual layers. Each spatial index indicates respectively for each spatial index cell of a corresponding virtual layer at least one candidate conductor of the corresponding virtual layer. The method includes creating optimal transition squares for the geometric point respectively for one or more consecutive virtual layers according to spatial indexes of the one or more consecutive virtual layers. The geometric point is located in one of the one or more consecutive virtual layers. The optimal transition squares are constrained by one or more candidate conductors of the one or more consecutive virtual layers. The method includes creating an optimal transition cube for the geometric point considering the one or more candidate conductors based on the optimal transition squares. The method includes calculating capacitance of the three-dimensional circuit using surfaces of the optimal transition cube.

Random-walk based capacitance extraction with optimized transition volumes

Extraction of capacitance values from a design of an electrical circuit can use a set of trained neural networks to generate extracted capacitance values from the circuit using a representation of the Green's function. A method can include the following operations: storing a machine learning model that includes a trained set of one or more neural networks that have been trained to calculate a representation of a Green's function to extract capacitance values from a design of a circuit having a set of conductors; applying, to the machine learning model, a set of inputs representing the set of conductors and their surrounding dielectric materials; encoding the set of inputs through a trained encoder to generate a latent space representation; calculating the values of the Green's function from the latent space representation through a dedicated trained neural network; and calculating the values of the gradient of the Green's function from the latent space representation through another dedicated trained neural network.

Capacitance extraction systems based on machine learning models

An apparatus for performing multi-tier domain pre-characterization for floating random walk capacitance extraction of a semiconductor structure includes a processor configured to recursively execute a floating random walk algorithm, over a plurality of points for a plurality of conductors, to permit determination of a potential at a plurality of points on a Gaussian surface around each of a plurality of conductors and determination of a coupling capacitance between the plurality of conductors, each iteration of the floating random walk algorithm comprising selection of a domain about an initial boundary point on the Gaussian surface of the respective one of the plurality of conductors and determination of a new boundary point on the new domain, from which a successive boundary point is selected with a corresponding successive domain centered thereabout, this process continuing until a corresponding successive domain terminates at a boundary having a known potential, whereupon the processor determines the potential at the initial boundary point on the Gaussian surface of the respective one of the plurality of conductors, wherein at least one domain during each floating random walk is matched by the processor to one of a plurality of pre-characterized domains, the matching of the selected domain with the one of a plurality of pre-characterized domains utilizing a smart sweep method, a single dielectric method, a two-dielectric method, a three-dielectric method or an averaging method.

Pre-computation system and method for transition domain characterization within a floating random walk based multi-dielectric capacitance extraction methodology

An apparatus for extracting capacitances of arbitrarily oriented three-dimensional interconnects includes a processor configured to recursively execute a floating random walk algorithm over a plurality of points for a plurality of conductors, to permit determination of a potential at a plurality of points on a Gaussian surface around each conductor and determination of a coupling capacitance between each conductor. Each iteration includes selecting an initial domain centered about an initial boundary point on a Gaussian surface of an initial conductor, determining a new boundary point on the initial domain from which a successive domain centered about the new boundary point may be selected, and determining a corresponding successive boundary point on the successive domain, each iteration continuing until the new boundary point or the corresponding successive boundary point terminates on a boundary having a known potential. Each selected domain may be rotated to align with the nearest conductor.

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Athens, Greece

Marios Visvardis