Digital Compensation of Timing Skew Mismatches in Time-Interleaved ADCs by Source Separation
Bibliographic record
Abstract
This paper proposes an adaptive all-digital background compensation of timing skew mismatches in time-interleaved analog-to-digital converters (TI-ADCs). The proposed approach uses the orthogonality between the desired component representing ideal samples of the TI-ADC input signal and the timing skew mismatch errors. Our technique is the first attempt to use a source separation mechanism to recover the desired component in the digital output of TI-ADC. A finite impulse response (FIR) filter implementing the Hilbert transform and two separate adaptive FIR (A-FIR) filters are exploited to implement this recovery process. The proposed technique works at frequencies close to the full admissible Nyquist frequency range, and its operation does not depend on the number of sub-ADCs (SADCs). This method operates without any extra SADC. The main cost introduced by the proposed compensation technique is the requirement of two adjustable FIR filters. This cost is proportional to the number of taps in these FIR filters that can be optimized according to the specific application. This technique is validated through various behavioral simulations and a field-programmable-gate-array (FPGA) implementation. With the proposed method, the signal-to-noise and distortion ratio (SNDR) is enhanced from 23.06 dB to more than 66.82 dB in a 12-bit TI-ADC when the standard deviation of timing skew mismatches is 10% of the TI-ADC’s sampling interval.
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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 | 0.000 | 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 | 0.001 |
| 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 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".