Yield prediction method and system of circuit based on Bayesian filter and resampling

Abstract

The invention discloses a yield prediction method and system of a circuit based on a Bayesian filter and resampling. The yield prediction method comprises the following steps: pre-sampling a parameter domain by adopting a Scrambled Sobol sequence to obtain a pre-sampling point; obtaining an initial failure domain and an initial non-failure domain according to the pre-sampling points, constructing initial sampling distribution, and performing iterative operation by taking the initial failure domain as a current failure domain; during iterative operation, a sampling center of a current failure domain is selected according to a corresponding rule to perform resampling, and classifier screening and SPICE simulation are performed to obtain a new failure domain; and if the quality factor of the current failure rate estimation value reaches the convergence standard, stopping iteratively outputting the unbiased estimator of the current failure rate, otherwise, taking the new failure domain as the current failure domain to continue the iterative operation. According to the method, the naive Bayes classifier is constructed by fully utilizing the information obtained by pre-sampling to classify and screen the sampling points, and whether follow-up simulation is carried out or not is determined according to the screening result, so that the SPICE simulation frequency is greatly reduced.

Description

technical field [0001] The present invention relates to the field of semiconductor and integrated circuit yield prediction technology, in particular to a method and system for predicting the yield rate of the semiconductor integrated circuit by performing circuit simulation based on the integrated circuit netlist and using an adaptive resampling algorithm based on a naive Bayesian filter . Background technique [0002] As integrated circuits enter the nanometer era, process variation has become a major challenge in the design and manufacture of integrated circuits. Circuit parameters such as the effective channel length and threshold voltage of transistors may deviate significantly from the nominal values ​​proposed by the designer due to uncertainties caused by many processes in the manufacturing process such as photolithography, chemical mechanical polishing, CMP), etc. The Monte Carlo (MC) method is known as the gold standard for repeatedly generating sampling samples to...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the technical problem that the yield prediction in the prior art needs to consume a large amount of computing resources, the present invention proposes a yield prediction method and system based on a Bayesian filter and resampling circuit

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