Leveraging Public Information to Fit a Compact Hot Carrier Injection Model to a Target Technology
Bibliographic record
Abstract
The design of countermeasures against integrated circuit counterfeit recycling requires the ability to simulate aging in CMOS devices. Electronic design automation tools commonly provide this ability; however, their models must be tuned for use with a specific target technology. This requires data which is ideally provided by a fab. It may also be collected from a set of purpose-built test devices, a costly and time-consuming process. Here we describe a novel, low-cost, and rapid approach to tuning such models. Our iterative method leverages public domain data sourced from published studies to fit an aging model. Results are statistically validated against the target technology’s specification. We demonstrate our approach by fitting a compact hot carrier injection degradation model for use with both core and I/O nMOSFETs from a specific 65 nm technology. Our resulting model parameter values are validated with a maximum error of 0.5% with a 99% confidence bound.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".