Signature Testing of Analog-To-Digital Converters
Bibliographic record
Abstract
When testing an analog-to-digital converter (ADC) by automatic test equipment (ATE), the latter is capable of performing extensive processing of output responses of the ADC. This allows detection of virtually any fault. However, the cost of ATE is quite high. As well, the external bandwidth of ATE is normally lower than the internal bandwidth of the ADC being tested, which makes it difficult to accomplish at-speed testing. It is important, therefore, to embed test hardware into ADC itself. The methods employed at ATE are complex and inconvenient for built-in realization. More advantageous are the methods exploiting accumulation of output responses. The size of the accumulator depends on the number of responses. In order to achieve greater fault coverage, this number is kept large, complicating the implementation. On the other hand, signature analysis used in digital systems testing is well suited for compaction of “lengthy” responses, and it is characterized by small hardware overhead and low aliasing probability. In this work, we apply signature analysis principle for compaction of output responses of an ADC. The permissible tolerance bounds for a fault-free ADC are determined and the aliasing rate is estimated. Examples are given.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".