Current testing: Dead or alive?
Bibliographic record
Abstract
Current, voltage and time (frequency) are the base parameters describing an electronic system. In the 1700's, Benjamin Franklin was one of the first experimenting with current tests, followed by many others shaping the current domain. In 1963 Frank Wanlass (Fairchild Semiconductor) planted the first seeds of using current testing as part of a structural approach to validate integrated circuits when publishing the concept of complementary-MOS (CMOS) logic circuitry. It occurred to him that a CMOS circuit would use very little power and that in standby; it would draw practically nothing — just the leakage current. It was therefore a fact that CMOS circuits with increased standby power consumption were defective. In 1981 Mark W. Levi demonstrated the concept of IDDQ testing (validating circuits by measuring and observing their quiescent supply current) in his ITC'1981 paper “CMOS is most Testable”. This paper kicked off a lot of research on IDDQ fault modeling, IDDQ defect detection capabilities, IDDQ and reliability, IDDQ efficiency. Much of that research happened in the late eighties — early nineties by “Chuck and Jerry”, exploring the benefits, followed by studies done by HP, IBM, TI, Philips, Alcatel, Ford Micro, … Since then IDDQ testing became synonym to current testing. Extensive research revealed the IDDQ capabilities. Despite its demonstrated defect detection capabilities and screening efficiency, it was not an easy way for IDDQ to make it to the production test floor. The initial lack of commercial available ATPG tools and suitable measurement solutions were the hurdles to take.
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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.010 | 0.030 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".