Design Techniques for Linearity in Time-Based Analog-to-Digital Converter
Bibliographic record
Abstract
Due to technology scaling, the design of the conventional-type analog-to-digital converter (ADC), which uses an operational amplifier as one of its building blocks, becomes more difficult. In this brief, new techniques to design time-based ADC (TADC), which uses a voltage-controlled oscillator (VCO), are proposed. The VCO is followed by a time-to-digital converter, implemented in a ΣΔ architecture. A novel architecture, using a multibit, nonlinear internal quantizer and a feedback digitalto-time converter, implemented by using phase interpolation, is employed to compensate the nonlinear transfer curve of the VCO. Dynamic element matching and calibration are used to improve the robustness of the TADC against mismatch. The TADC uses an implicit sample and hold that relaxes the bounds on input frequency. A TADC implemeDue to technology scaling, the design of the conventional-type analog-to-digital converter (ADC), which uses an operational amplifier as one of its building blocks, becomes more difficult. In this brief, new techniques to design time-based ADC (TADC), which uses a voltage-controlled oscillator (VCO), are proposed. The VCO is followed by a time-to-digital converter, implemented in a ΣΔ architecture. A novel architecture, using a multibit, nonlinear internal quantizer and a feedback digital-to-time converter, implemented by using phase interpolation, is employed to compensate the nonlinear transfer curve of the VCO. Dynamic element matching and calibration are used to improve the robustness of the TADC against mismatch. The TADC uses an implicit sample and hold that relaxes the bounds on input frequency. A TADC implemented in 0.13-μm CMOS technology achieves a measured signal-to-noise + distortion ratio of 60.2 dB and a dynamic range of 11 b for a bandwidth of 2 MHz.nted in 0.13-μm CMOS technology achieves a measured signal-to-noise + distortion ratio of 60.2 dB and a dynamic range of 11 b for a bandwidth of 2 MHz.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".