Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Automātiska testēšanas paraugu ģenerēšana× | Monte Carlo procesu variāciju analīze× | |
|---|---|---|
| Nozare | Elektrotehnika | Elektrotehnika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1966 | 2003 |
| Autors≠ | J. Paul Roth | George S. Fishman, Sani R. Nassif |
| Tips≠ | Automated fault-detection test vector generation | Probabilistic modeling of semiconductor manufacturing variability |
| Pirmavots≠ | Abramovici, M., Breuer, M. A., & Friedman, A. D. (1990). Digital Systems Testing and Testable Design. Computer Science Press. link ↗ | Fishman, G. S. (1996). Monte Carlo: Concepts, Algorithms, and Applications. Springer-Verlag. DOI ↗ |
| Citi nosaukumi | ATPG, Test pattern generation, Fault-based testing | Monte Carlo simulation, Process variation analysis, PVT analysis |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | Automatic Test Pattern Generation (ATPG) is the automated creation of test vectors that detect manufacturing defects in digital circuits. Pioneered by Roth in 1966, ATPG systematically finds inputs that make stuck-at faults observable at outputs, enabling comprehensive fault detection. ATPG is critical for semiconductor manufacturing: enabling high test coverage ensures only good chips ship and identifies manufacturing process issues. | Monte Carlo Process Variation analysis quantifies the impact of manufacturing uncertainties on circuit performance using statistical sampling. As semiconductor technology scales, process variations (gate length, oxide thickness, dopant fluctuations) create significant uncertainties in delay, power, and leakage. Monte Carlo methods sample the random variation space, enabling statistical characterization of yield, timing margins, and reliability. Essential for modern technology nodes. |
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