A Digital-to-Time Converter for Time-Mode Successive-Approximation Register Analog-to-Digital Converters
Bibliographic record
Abstract
The 8-bit digital-to-time converter (DTC) to be used for a time-mode successive-approximation register analog-to-digital converter (SAR ADC) with a minimum power consumption and silicon area is presented. The architecture and the drawbacks of a conventional voltage-mode SAR ADC are discussed. The principle of time-mode circuits and benefits of their applications to mixed-signal circuits are explained. The architecture of a time-mode SAR ADC is presented. The need for an area and power-efficient DTC to be used for a time-mode SAR ADC is discussed. The principle of a DTC is explained and prior works on a DTC are reviewed. The principle of a phase interpolator (PI), to be used for a DTC, is explained and prior works on digital PIs are reviewed. The design of the proposed DTC is presented. Each block of the proposed DTC is explained using schematic and layout views. Optimal slope of the input of the PI and the condition for linear phase interpolation are investigated. Simulation results of the proposed DTC designed in TSMC 65 nm 1.0 V CMOS technology are provided. According to simulation results with BSIM4.4 device models only, the time resolution of 0.33 ps, a maximum operation frequency of 2.53 G Hz, the power consumption of 1.38 mW, and peak differential nonlinearity (DNL) and integral nonlinearity (INL) less than 0.14 least significant bit (LSB) and 0.49 LSB, respectively, for a nominal process (TT) and a temperature condition (27 C°) are achieved.
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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) | 0.012 | 0.005 |
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".