A Two-Phase Multi-Bit Incremental ADC With Variable Loop Order
Bibliographic record
Abstract
This brief presents a two-phase multi-bit incremental analog-to-digital converter (IADC) with variable loop order. In the 1stphase, the loop filter works as a 1st-order topology. In the 2ndphase, the loop reconfigures to a 3rd-order structure, aiming to get the signal-to-quantization-noise ratio (SQNR) boosted quickly within a few clock cycles. Such a two-phase scheme with variable loop-order combines the features of the KT/C noise suppression and high effectiveness of data weighting averaging (DWA) presented by the 1st-order IADC and fast accumulation obtained from the high-order mode. Thereby, with little additional circuitry effort, the proposed IADC improves DWA effectiveness while mitigating the thermal noise penalty when compared with a pure high-order IADC. The proposed architecture is analytically analyzed and exemplarily simulated. Moreover, a design guideline is provided to optimize the allocation of the clock cycles of two phases, thus balancing various significant parameters. Based on the guideline, a circuit-level simulation of an exemplary 1st-to-3rdorder IADC was carried out in a 65-nm CMOS process to confirm the results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".