A new digital test approach for analog-to-digital converter testing
Bibliographic record
Abstract
A fully digital built-in self-test (BIST) for analog-to-digital converters is presented in this paper. This test circuit is capable of measuring the DNL, INL, offset error and gain error, and mainly consists of several registers and some digital subtracters. The main advantage of this BIST is the ability to test DNL and INL for all codes in the digital domain, which in turn eliminate the necessity of calibration. On the other hand, some parts of the analog-to-digital converter with minor modifications are used in the BIST simultaneously. This also reduces the area overhead and the cost of the test. The proposed BIST structure presents a compromise between test accuracy, area overhead and test cost. The BIST circuitry has been designed using CMOS 1.5 /spl mu/m technology. The simulation results of the test show that it can be applied to medium resolution analog-to-digital converter or high resolution pipelined analog-to-digital converter. The presented BIST shows satisfactory results for 9-bit pipelined analog-to-digital converter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".