Statistical Timing for Parametric Yield Prediction of Digital Integrated Circuits

Uncertainty in circuit performance due to manufacturing and environmental variations is increasing with each new generation of technology. It is therefore important to predict the performance of a chip as a probabilistic quantity. This paper proposes three novel path-based algorithms for statistical timing analysis and parametric yield prediction of digital integrated circuits. The methods have been implemented in the context of the EinsTimer static timing analyzer. The three methods are complementary in that they are designed to target different process variation conditions that occur in practice. Numerical results are presented to study the strengths and weaknesses of these complementary approaches. Timing analysis results in the face of statistical temperature and V_dd variations are presented on an industrial ASIC part on which a bounded timing methodology leads to surprisingly wrong results