Integrated circuit simulation data correlation modeling method and apparatus

Abstract

The present invention belongs to the field of integrated circuit design automation, and particularly relates to an integrated circuit simulation data correlation modeling method and apparatus based onthe correlation clustering and covariance contraction technology. The method comprises: obtaining circuit simulation data required for modeling; constructing a primitive multivariate normal distribution according to the data; and modifying the distribution through the correlation clustering and covariance contraction technology to obtain a correlation model expressed by the modified multivariatenormal distribution. According to the technical scheme of the present invention, the reliability and accuracy of the correlation model are improved, so that the correlation model of the circuit simulation data can be applied to circuits of any scale, and accuracy and efficiency the algorithm developed by the model can be ensured.

Description

technical field [0001] The invention belongs to the field of integrated circuit design automation, and in particular relates to modeling of correlation of integrated circuit simulation data. Specifically, it relates to a correlation modeling method and device for integrated circuit simulation data based on correlation clustering and covariance shrinkage techniques. Background technique [0002] The prior art discloses that since integrated circuits are inevitably affected by process conditions during the manufacturing process, the physical parameters of each transistor, such as doping concentration, gate oxide layer thickness, surface charge, etc., will fluctuate randomly. Therefore, the electrical properties of the transistor Characteristics such as threshold voltage, leakage current, etc. and circuits composed of these transistors also have a certain degree of randomness in performance (such as delay, gain, etc.). With the development of integrated circuit process technol...

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Problems solved by technology

For a given set of multivariate data, the correlation of any two dimensions can be determined simply by unbiased estimation or maximum likelihood estimation of the covariance matrix of the multivariate data; however, due to the limitation of simulation cost, the simulation of integrated circuits The data often have the characteristics of high dimensionality and few samples. In this case, the estimated value of the covariance matrix is ​​unstable and has a large error, which is usually not directly used for computer processing.

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