Statistical timing analysis method and device based on improved adaptive random configuration method

Abstract

The invention relates to a statistical static timing analysis method. In the method, the MAX approximation problem in the statistical static timing analysis method is solved through an adaptive random configuration method (MASCM) based on an improved nested sparse grid integration method. In the MASCM, the MAX approximation is divided into two classes according to input end conditions, namely a linear input condition and a nonlinear input condition, wherein under the linear input condition, an input end with the maximum mean value is selected as output of the MAX; and under the nonlinear input condition, the orthogonal multinomial expansion coefficient is calculated by the improved nested sparse grid integration method. The improved nested sparse grid integration method improves the utilization rate of integration points in the random configuration method, guarantees the integration accuracy, reduces the number of configuration points and reduces the calculation time for statistical static timing analysis. Compared with the conventional methods, the provided MASCM has the equivalent calculation accuracy, while the needed number of integration points and calculation time are greatly reduced.

Description

Technical field: [0001] The invention belongs to the field of integrated circuits, and in particular relates to a statistical static timing analysis method based on an improved self-adaptive random configuration method. Background technique [0002] As the feature size of integrated circuits enters the nanometer level, the impact of process deviation on circuit performance is becoming more and more serious. In order to analyze the impact of process deviation on the timing of integrated circuits, Statistical Static Timing Analysis (SSTA) was introduced into timing analysis. "Statistical Time-Series Analysis: From Fundamentals to State-of-the-art", 27(4), pp. 589-607. In statistical static timing analysis, due to the influence of random process deviation, the delay of the circuit is no longer a definite value, but a random variable with a certain distribution. Statistical static timing analysis analyzes circuit timing by passing a random distribution of delays through the ci...

AI Technical Summary

Problems solved by technology

In practical applications, the occurrence of strong nonlinear conditions may be around 30%, while the number of random variables is between a dozen and dozens, which makes it impossible for us to apply the tensor integration method to solve MAX under strong nonlinear conditions. Operation Approximation Problem

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