A 14.5-Bit ENOB, 10MS/s SAR-ADC With 2<sup>nd</sup> Order Hybrid Passive-Active Resonator Noise Shaping
Bibliographic record
Abstract
A new 2nd order noise shaping (NS) based successive approximation register (SAR) ADC is presented in this paper. In comparison to earlier research, this paper considers hybrid passive-active integrators to compensate for the phase error of the passive integrator. To realize the resonator noise shaping in high-speed asynchronous SAR-ADC, the hybrid passive-active sigma-delta modulator (SDM) is introduced as a multi-input feedforward loop filter to overcome the noise barrier of the conventional asynchronous SAR-ADC generated from the CDAC, quantizer, and dynamic comparator. The proposed noise shaping technique significantly reduces the ADC power consumption and area compared with the active SDM noise shaping approach while overcoming the shortcomings of passive SDM, such as large-area penalty, low resolution, and low speed. It consists of a very low power forward gain G and a positive feedback path across a 1st order passive switch capacitor (SC) integrator to desensitize the capacitor ratios under PVT variations. Extensive circuits simulation verifications and system-level results have been used to validate the effectiveness of the proposed NS SAR-ADC. The simulation results show that the proposed SAR ADC consumes 0.88mW at maximum speed, with an SNDR of 89.43 dB and SFDR 98.64 dB within 0.1 fs oversampling frequency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".