Advanced Noise-Shaping SAR ADCs Utilizing Single-Capacitor Arbitrary-Resolution DACs for Miniaturized Neural Interfaces
Bibliographic record
Abstract
This paper presents the development and validation of a high-performance Noise-Shaping (NS) Successive-Approximation-Register (SAR) Analog-to-Digital Converter (ADC) specifically tailored for low-power, high-density Implantable Neural Interfaces (INIs). The proposed design introduces a novel approach to the Digital-to-Analog Converter (DAC) formation within the NS-SAR ADC, utilizing a single capacitor to achieve arbitrarily-high resolution. It minimizes distortion due to mismatches of conventionally used capacitor banks and reduces the NS-SAR ADC area, enabling higher resolution without a proportional increase in area. Furthermore, our design addresses the challenges of kickback noise and lossy integration, improving overall performance and signal integrity during the conversion process. Simulation and experimental results substantiate the performance of the proposed noise-shaping SAR ADC. The ADC demonstrated a high peak signal-to-noise-and-distortion ratio (SNDR) of 89 dB. Moreover, it proved its robustness across different frequencies and capacitor mismatches, maintaining an SNDR of 70dB even with a 10% mismatch of the capacitors.
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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 | 0.000 |
| 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) | 0.000 | 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".