Fault Detection for ASIC Design Reliability on Resistive Delay Faults and Strength-Based Soft-Errors
Bibliographic record
Abstract
Soft-errors (SEs) and delay faults (DFs) frequently occur in modern high-density, high-speed, low-power VLSI circuits. Therefore, SE hardened design and DF testing are essential. This thesis introduces two novel methods for soft-error detection and delay fault propagation in nanometre technology. A new idea is proposed to propagate those delay faults that are not causing logic failure at the site of the defect, but the delay makes the circuit more prone to soft-errors that manifest the effect of delay faults. This approach propagates the fault from the fault location by mapping a nine-valued voltage model on top of a five-valued voltage model to convert delay faults to static faults. This original idea reduces the complexity of delay fault propagation. This thesis introduces an original approach toward soft-error detection based on the strength violation in the circuit. This research shows that transient pulses of less than threshold voltage will cause soft-errors without altering the logic value at the strike location. This method will increase the Soft-Error Rates (SER) for all existing methods if strength-based Soft-Error detection will be considered. The offered approach uses a novel coding system that carries both logic and strength which applies to certain logic functions that are sensitive to strength variations. A wide range of soft-errors are the result of strength violation in switch-level that have never been investigated before.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".