Data-driven fault model development for superconducting logic
| Title | Data-driven fault model development for superconducting logic |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, M., Wang, F., Gupta, S. |
| Conference Name | 2020 IEEE International Test Conference (ITC) |
| Date Published | Nov. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-9113-3 |
| Keywords | Analytical models, Circuit faults, clean slate, Collaboration, fault diagnosis, Human Behavior, Integrated circuit modeling, Manuals, Metrics, policy-based approach, pubcrawl, resilience, Resiliency, Semiconductor device modeling, simulation |
| Abstract | Superconducting technology is being seriously explored for certain applications. We propose a new clean-slate method to derive fault models from large numbers of simulation results. For this technology, our method identifies completely new fault models - overflow, pulse-escape, and pattern-sensitive - in addition to the well-known stuck-at faults. |
| URL | https://ieeexplore.ieee.org/document/9325220 |
| DOI | 10.1109/ITC44778.2020.9325220 |
| Citation Key | li_data-driven_2020 |