Coverage-driven mixed-signal verification of smart power ICs in a UVM environment
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
The complexity of integrated circuits is continuously increasing, leading to a growing demand for methodologies that offer comprehensive mixed-signal verification concepts. However, compared to the highly automated verification methodologies in the digital domain, pre-silicon verification in the analog domain usually implies a substantial amount of manual work and computational effort. In order to meet the rising challenges, various attempts were made to extend well-established approaches from the field of digital verification to also enable systematic mixed-signal verification. However, no methodology could be identified that meets our requirements for high reusability and maintainability, tool independence as well as capabilities for functional coverage collection. For this reason, we propose a mixed-signal verification methodology that covers the aforementioned as well as additional aspects required for a successful coverage closure. The presented concept is applied to a smart power application to demonstrate its potential and outline the gained benefits.
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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 | 0.001 | 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