Gerabaldi: A Temporal Simulator for Probabilistic IC Degradation and Failure Processes
Bibliographic record
Abstract
Wear-out reliability in integrated circuits is becoming an increasingly complex topic, with emerging high-reliability markets demanding stricter requirements, diverse workloads making stress characterization challenging, and sub-5nm device scaling aggravating variability in degradation processes. Efforts to tackle these complexities can benefit greatly from sophisticated techniques that effectively capture the variable and uncertain nature of semiconductor wear-out mechanisms. True-to-life stochastic modelling and computational Bayesian inference offer promising avenues in this pursuit but are difficult to leverage without a framework for specifying and evaluating wear-out models that capture this probabilistic information. We present a temporal wear-out simulator, Gerabaldi, as a foundation for enabling these statistical techniques for integrated circuit reliability engineering. The simulator introduces novel capabilities including layered stochastic parameter modelling, fully agnostic design enabling custom degradation model and stress test specifications, and wear-out model definition forms compatible with modern computational Bayesian inference frameworks. Here, we frame Gerabaldi within the context of existing wear-out analysis methods. We then present its key design features and two detailed example applications to illustrate the simulator’s capabilities.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".