Pattern Selection for Testing of Deep Sub-Micron Timing Defects
Proceedings of the conference on Design, automation and test in Europe - Volume 2
Reducing Pattern Delay Variations for Screening Frequency Dependent Defects
VTS '05 Proceedings of the 23rd IEEE Symposium on VLSI Test
Advances in Computation of the Maximum of a Set of Random Variables
ISQED '06 Proceedings of the 7th International Symposium on Quality Electronic Design
High Quality Test Vectors for Bridging Faults in the Presence of IC's Parameters Variations
DFT '07 Proceedings of the 22nd IEEE International Symposium on Defect and Fault-Tolerance in VLSI Systems
Test-Pattern Grading and Pattern Selection for Small-Delay Defects
VTS '08 Proceedings of the 26th IEEE VLSI Test Symposium
Compact Delay Test Generation with a Realistic Low Cost Fault Coverage Metric
VTS '09 Proceedings of the 2009 27th IEEE VLSI Test Symposium
Process variation-aware test for resistive bridges
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Random variability modeling and its impact on scaled CMOS circuits
Journal of Computational Electronics
Massive statistical process variations: A grand challenge for testing nanoelectronic circuits
DSNW '10 Proceedings of the 2010 International Conference on Dependable Systems and Networks Workshops (DSN-W)
On Delay Fault Testing in Logic Circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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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.