An Area-Efficient High-Resolution Segmented ΣΔ-DAC for Built-In Self-Test Applications
Bibliographic record
Abstract
Sigma–delta ($\Sigma \Delta$) digital-to-analog converters (DACs) are commonly used to realize programmable dc voltage generators in built-in self-test (BIST) schemes, largely on account of their high linearity. Unfortunately, such$\Sigma \Delta $-DACs require large amounts of digital memory and a reconstruction filter with a large silicon area footprint. In this article, a segmented DAC architecture is proposed. The proposed architecture is realized using two sub-DACs, where both are$\Sigma \Delta $-based. This DAC provides two advantages: filter footprint size reduction and memory savings, thereby allowing for a simpler BIST solution. The benefits of using the segmented$\Sigma \Delta $-DAC are shown using two experimental prototypes. The first is an IC prototype design using the TSMC 65-nm CMOS technology. It will be shown that the IC achieves 12 bits of resolution from 1020 memory elements, whereas an unsegmented$\Sigma \Delta $-DAC uses 4095 elements to obtain the same resolution. This is about a 75% savings in memory elements. The segmented prototype occupies 0.5 mm2of silicon area, whereas the unsegmented design occupies 0.77 mm2for the same resolution. A 35% silicon area savings compared with its unsegmented counterpart. A second prototype implements the segmented$\Sigma \Delta $-DAC architecture using discrete components. The discrete prototype achieves 16 bits of resolution from 1020 memory elements, whereas the unsegmented counterpart uses 65 535 bits for the same resolution. This is a 98% saving in memory elements.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".