Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit reliability.BTI results in threshold voltage increases on CMOS transistors,c...Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit reliability.BTI results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic circuits.The amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design time.In this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI degradation.It consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability distributions.This method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.展开更多
基金3the High Performance Computing Center of Shanghai University,Shanghai Engineering Research Center of Intelligent Computing System(19DZ2252600)supported by State Key Laboratory of Computer Architecture(Institute of Computing Technology,Chinese Academy of Sciences)(CARCH201909)。
文摘Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit reliability.BTI results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic circuits.The amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design time.In this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI degradation.It consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability distributions.This method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.
