Special session 12B: Panel post-silicon validation & test in huge variance era
Bibliographic record
Abstract
At the 1999 ITC, Pat Gelsinger from Intel delivered an important keynote address where he outlined the need for a low-pin count tester with lower performance pin electronics to meet the stringent test cost requirements of a billion transistor machine. At the 2009 ITC, engineers from AMD came forward with an I/O test solution that is believed to meet the Intel challenge using a cash-resident self-testing strategy combined with an external low-pin count tester. How can we drive major challenges to post-silicon validation and in huge variance era? Technology scaling enables us to trade off amplitude resolution for time resolution. Accordingly, both internal and external tests, some of which use low-pin count testers, are also shifting from voltage centric tests to timing centric tests. How can time resolution be used to push the timing centric tests beyond current limitations? How can spatial resolution be realized to enhance yields in terms of both die-to-die variations and within-die variations? What is necessary to provide robust on-chip solutions subject to huge variations, which may be combined with an external low-pin count tester?
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.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.099 | 0.063 |
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".