Compensation of delta-sigma modulators: stabilization, signal restoration and integrated circuits
Bibliographic record
Abstract
We propose novel architectures and techniques for the stabilization of continuous-time delta-sigma modulators, the on-line restoration of corrupted oversampled data, and the implementation of high speed delta-sigma modulators in complementary metal-oxide semiconductor integrated circuit technology. Specifically, the contributions of this work are threefold: (1) A novel architecture for the stabilization of continuous-time delta-sigma modulators of arbitrary order is proposed. The approach features a guarantee of stability under certain assumptions and accommodates quantizers incorporating any number of levels and dithering. An estimation method to predict long-term SNR performance of the stabilized delta-sigma modulator is provided. We show that first-order noise-shaping can be achieved. (2) New measures for the management of instability in delta-sigma modulation are proposed. Digital state estimation techniques based on observer theory adapted to delta-sigma modulation are developed and simulated. Restorative algorithms suitable for multiplier-free digital implementation are also provided and simulated. Significant SNR recoveries are shown to be possible. (3) A compensated fifth-order single-bit continuous-time delta-sigma modulator integrated circuit for analog-to-digital conversion is designed, laid-out, fabricated (in a 0.35-micron CMOS technology) and tested. The basic operation of our soft-resetting technique is demonstrated in steady-state. Nominal performance of 56-dB SNDR and 51-dB DR in a signal bandwidth from 100–500 kHz is achieved. The presence of a non-ideal do offset is discussed and a possible remedy is proposed. The main purpose of the IC is for demonstrating that advanced switching control techniques can be implemented in a practical fashion. The chip does not represent a contribution to the state-of-the-art in nominal SNDR and DR modulator performance.
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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 | 0.000 |
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".