Bridging fault diagnostic tool based on Δ <i>I</i> <sub>DDQ</sub> probabilistic signatures, circuit layout parasitics and logic errors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A diagnostic tool for bridging faults combining three different data sources is presented. The first data source is a set of IDDQ measurements used to identify the most probable fault type. The second source is a list of parasitic capacitances extracted from layout and used to create a list of realistic potential bridging fault sites. The third source is logical faults detected at the primary outputs (including scan flip flops), used to limit the number of suspected gates. The combination of these data significantly reduces the number of potential fault sites to consider in the diagnosis process. Simulation results confirm that the number of potential bridging fault sites is reduced from O(N2) to less than O(N), where N is the number of nodes in the circuit. The tool therefore converges quickly towards the solution while using less resources. A new technique is also introduced to estimate the additional delay caused by the diagnosed bridging fault based on the diagnostic results. Performing this estimation allows us to confirm the previous diagnosis results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it