Graph partition based path selection for testing of small delay defects

Critical path selection plays an important role in testing of small delay defects (SDD). For some timing-balanced circuits, the numbers of candidate critical paths may be very large, and this will make Monte Carlo simulation based statistical timing analysis very inefficient. A fast path selection approach based on graph partition is proposed in this paper. First, a critical path graph (CPG) is generated to implicitly enumerate almost all candidate critical paths, and then the CPG is partitioned into several sub graphs which contain limited numbers of paths using two graph partition approaches. After that, Monte Carlo simulation is applied on each sub graph for path selection. At last, according to the partition topology of the CPG and path sets selected from each sub graph, a path set for the original CPG is generated using Union and Cartesian product operations for testing SDDs. Experimental results show that for circuits containing large numbers of candidate critical paths, the proposed path selection approach can reduce the CPU time significantly and maintain a higher probability of capturing delay failures compared to path selection methods based on general Monte Carlo simulation.

[1]  Robert C. Aitken,et al.  Nanometer Technology Effects on Fault Models for IC Testing , 1999, Computer.

[2]  D. Blaauw,et al.  Criticality Aware Latin Hypercube Sampling for Efficient Statistical Timing Analysis , 2007 .

[3]  David Blaauw,et al.  Statistical Timing Analysis: From Basic Principles to State of the Art , 2008, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[4]  Mohab Anis,et al.  On efficient Monte Carlo-based Statistical Static Timing Analysis of digital circuits , 2008, 2008 IEEE/ACM International Conference on Computer-Aided Design.

[5]  Rajiv V. Joshi,et al.  Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events , 2006, 2006 43rd ACM/IEEE Design Automation Conference.

[6]  Alper Demir,et al.  Smart Monte Carlo for Yield Estimation , 2006 .

[7]  Lou Scheffer The Count of Monte Carlo , 2004 .

[8]  Melvin A. Breuer,et al.  New Validation and Test Problems for High Performance Deep Sub-micron VLSI Circuits , 2000, VLSI Design 2000. Wireless and Digital Imaging in the Millennium. Proceedings of 13th International Conference on VLSI Design.

[9]  Jinjun Xiong,et al.  Variation-aware performance verification using at-speed structural test and statistical timing , 2007, 2007 IEEE/ACM International Conference on Computer-Aided Design.

[10]  D. Blaauw,et al.  Statistical delay computation considering spatial correlations , 2003, Proceedings of the ASP-DAC Asia and South Pacific Design Automation Conference, 2003..

[11]  Keith Baker,et al.  Defect-based delay testing of resistive vias-contacts a critical evaluation , 1999, International Test Conference 1999. Proceedings (IEEE Cat. No.99CH37034).

[12]  Rajendran Panda,et al.  Statistical delay computation considering spatial correlations , 2003, ASP-DAC '03.

[13]  Kwang-Ting Cheng,et al.  Critical path selection for delay fault testing based upon a statistical timing model , 2004, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[14]  Kaushik Roy,et al.  Test challenges for deep sub-micron technologies , 2000, Proceedings - Design Automation Conference.