Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Автоматическая генерация тестовых шаблонов× | Процессная вариативность Монте-Карло× | |
|---|---|---|
| Область | Электротехника | Электротехника |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1966 | 2003 |
| Автор метода≠ | J. Paul Roth | George S. Fishman, Sani R. Nassif |
| Тип≠ | Automated fault-detection test vector generation | Probabilistic modeling of semiconductor manufacturing variability |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | ATPG, Test pattern generation, Fault-based testing | Monte Carlo simulation, Process variation analysis, PVT analysis |
| Связанные | 3 | 3 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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