Efficient variation-aware statistical dynamic timing analysis for delay test applications

Increasing parameter variations, caused by variations in process, temperature, power supply, and wear-out, have emerged as one of the most important challenges in semiconductor manufacturing and test. As a consequence for gate delay testing, a single test vector pair is no longer sufficient to provide the required low test escape probabilities for a single delay fault. Recently proposed statistical test generation methods are therefore guided by a metric, which defines the probability of detecting a delay fault with a given test set. However, since runtime and accuracy are dominated by the large number of required metric evaluations, more efficient approximation methods are mandatory for any practical application. In this work, a new statistical dynamic timing analysis algorithm is introduced to tackle this problem. The associated approximation error is very small and predominantly caused by the impact of delay variations on path sensitization and hazards. The experimental results show a large speedup compared to classical Monte Carlo simulations.