| Autor: |
T. Nigam, K. Fisher, Jack M. Higman, M. Ahosan ul karim, L. Peters, M. Luque, J. Dyck, Sriram Balasubramanian, Yuncheng Song |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 IEEE International Integrated Reliability Workshop (IIRW). |
| Popis: |
We developed an infrastructure to simulate SRAM Vmin shift from time-zero (TO) to end-of-life (EOL) by parts utilizing the Hierarchical Monte Carlo tool under Mentor’s Solido Variation Designer environment. This tool applies machine learning (ML) to rapidly verify full-chip memories with perfect SPICE accuracy. This allows us to simulate a large number of SRAM parts and obtain a better description of the statistics. The TO Vmin and Vmin shift distribution, obtained from the simulation, provide a way to project SRAM fail rate up to the stringent requirement of |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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