Method and apparatus for creating an extraction model using Bayesian inference
First Claim
1. A method of constructing a model for estimating at least one electrical characteristic for an extraction sub-problem, said method comprising:
- identifying a set of physical measurements of integrated circuit components that define said extraction sub-problem;
selecting a set of training cases for said specific extraction sub-problem, each of said training cases including an associated set of said physical measurements;
solving said specific extraction sub-problem for each of said training cases using said associated set of physical measurements as an input to an accurate physics based model to generate an associated output; and
training a machine-learning model with Bayesian inference using said associated set of physical measurements and associated outputs as training data.
2 Assignments
0 Petitions
Accused Products
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 system them uses the created training sets to train neural networks that will be used to model the extraction problems. Bayesian inference is used to train the neural networks 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.
-
Citations
20 Claims
-
1. A method of constructing a model for estimating at least one electrical characteristic for an extraction sub-problem, said method comprising:
-
identifying a set of physical measurements of integrated circuit components that define said extraction sub-problem;
selecting a set of training cases for said specific extraction sub-problem, each of said training cases including an associated set of said physical measurements;
solving said specific extraction sub-problem for each of said training cases using said associated set of physical measurements as an input to an accurate physics based model to generate an associated output; and
training a machine-learning model with Bayesian inference using said associated set of physical measurements and associated outputs as training data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
-
-
12. A computer-readable medium, said computer-readable medium comprising a set of instructions for constructing a model for estimating at least one electrical characteristic for an extraction sub-problem by performing the steps of:
-
identifying a set of physical measurements of integrated circuit components that define said extraction sub-problem;
selecting a set of training cases for said specific extraction sub-problem, each of said training cases including an associated set of said physical measurements;
solving said specific extraction sub-problem for each of said training cases using said associated set of physical measurements as an input to an accurate physics based model to generate an associated output; and
training a machine-learning model with Bayesian inference using said associated set of physical measurements and associated outputs as training data. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
-
Specification