High-Speed 16-Bit SAR-ADC Design at 500 MS/s with Variable Body Biasing for Sub-Threshold Leakage Reduction
Bibliographic record
Abstract
In this study, a high-performance 16-bit, 500 MS/s successive approximation register analog-to-digital con-verter (SAR-ADC) with variable body biasing (VBB) for re-ducing sub-threshold leakage is designed and optimized. The suggested ADC architecture makes use of a voltage threshold complementary metal-oxide-semiconductor (VTCMOS) cir-cuit with Widlar current mirror technology to efficiently con-sume 39.2 μW at an operating voltage of 1.0 V. Notably, the optimized ADC achieves outstanding performance measures, such as a signal-to-noise and distortion ratio (SNDR) of 97 dB and a total harmonic distortion (THD) of -97.97 dB, which are crucial markers of the ADC's accuracy and fidelity. An over-view of the growing need for high-resolution ADCs in contem-porary high-speed data conversion systems opens the study. The main goal of this effort is to improve overall ADC per-formance and tackle the problem of sub-threshold leakage. The Widlar current mirror technology and the VTCMOS cir-cuit are integrated for enhanced linearity, decreased current mismatch errors, and minimized leakage current. This inte-gration is highlighted in the full explanation of the ADC de-sign. The advent of the VBB approach as a successful method of leakage reduction is a significant contribution to this re-search. The theoretical foundations and workings of the VBB technique are discussed, and thorough simulations and tests are used to assess how the VBB technique affects leakage cur-rent and circuit performance. The SAR-ADC design and simulations were carried out using Cadence Virtuoso soft-ware.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".