Method and apparatus for performing extraction using machine learning
First Claim
1. A method of extracting electrical characteristics from an integrated circuit layout, said method comprising:
- dividing said integrated circuit layout into at least one extraction sub problem;
determining a set of physical parameters that define said extraction sub problem;
supplying said set of physical parameters to an extraction sub problem model built with machine learning; and
calculating at least one electrical characteristic for said extraction sub problem by analyzing said set of physical parameters with said extraction sub problem model built with said machine learning without performing a fuzzy comparison of said set of physical parameters with a set of physical parameters from a solved extraction problem.
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Abstract
A system for using machine-learning to create a model for performing integrated circuit layout extraction is disclosed. The system of the present invention has two main phases: model creation and model application. The model creation phase comprises creating one or more extraction models using machine-learning techniques. First, a complex extraction problem is decomposed into smaller simpler extraction problems. Then, each smaller extraction problem is then analyzed to identify a set of physical parameters that fully define the smaller extraction problem. Next, models are created using machine learning techniques for all of the smaller simpler extraction problems. The machine learning is performed by first creating training data sets composed of the identified parameters from typical examples of the smaller extraction problem and the answers to those example extraction problems as solved using a highly accurate physics-based field solver. The training sets are then used to train the models. In one embodiment, neural networks are used to model the extraction problems. Bayesian inference is employed by one embodiment in order to train the neural network models. Bayesian inference may be implemented with normal Monte Carlo techniques or Hybrid Monte Carlo techniques. After the creation of a set of models for each of the smaller simpler extraction problems, the machine-learning based models may be used for extraction.
49 Citations
20 Claims
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1. A method of extracting electrical characteristics from an integrated circuit layout, said method comprising:
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dividing said integrated circuit layout into at least one extraction sub problem;
determining a set of physical parameters that define said extraction sub problem;
supplying said set of physical parameters to an extraction sub problem model built with machine learning; and
calculating at least one electrical characteristic for said extraction sub problem by analyzing said set of physical parameters with said extraction sub problem model built with said machine learning without performing a fuzzy comparison of said set of physical parameters with a set of physical parameters from a solved extraction problem. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 18)
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11. A computer readable medium, said computer readable medium comprising an arranged set of computer instructions for:
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dividing an integrated circuit layout into at least one extraction sub problem;
determining a set of physical parameters that define said extraction sub problem;
supplying said set of physical parameters to an extraction sub problem model built with machine learning; and
calculating at least one electrical characteristic for said extraction sub problem by analyzing said set of physical parameters with said extraction sub problem model built with said machine learning without performing a fuzzy comparison of said set of physical parameters with a set of physical parameters from a solved extraction problem. - View Dependent Claims (12, 13, 14, 15, 16, 17, 19, 20)
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Specification
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- Resources
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Current AssigneeCadence Design Systems Incorporated (Cadence Group)
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Original AssigneeCadence Design Systems Incorporated (Cadence Group)
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InventorsTeig, Steven, Chatterjee, Arindam
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Primary Examiner(s)Siek, Vuthe
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Assistant Examiner(s)Lin, Sun James
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Application NumberUS10/062,264Time in Patent Office1,111 DaysField of Search716/5, 716/4, 716/6, 716/1US Class Current716/115CPC Class CodesG06F 30/367 Design verification, e.g. u...
