Bibliographic record
Abstract
An all-digitally implemented 1st order and a 2nd order time-mode ΔΣ ADCs are proposed and presented in this dissertation. Each proposed ΔΣ ADC consists of a voltage-to- time integration converter, a seven-stage gated ring oscillator functioning as a 3-bit quantizer, and a 7-stage digital differentiator that provides noise-shaping and frequency feedback. The 2nd order architecture differs from the 1st order by cascading two digital differentiators. The 2nd order design improves noise-shaping characteristic and SNDR. However it does not effectively suppress the harmonic tones due to the non-linear effect of the circuit components. Thus a detailed analysis of the nonlinear characteristics of the modulator is conducted. Designed in IBM 130 nm 1.2 V CMOS technology and with a 100 kHz 100 mV input, the 1st order time-mode ΔΣ ADC exhibits an SNDR of 45.5 dB over 0.4 MHz bandwidth with power dissipation of 1.1mW. In comparison, the 2nd order ADC provides 54.8 dB SNDR, which equivalently offers an ENOB of 8.8 and it consumes 1.45 mW RMS power. The figure- of-merit of the 2nd order time-mode ΔΣ ADC is 407 pJ/step. Since the order of the system cannot be increased by simply cascading more differentiator stages, a time-mode ΔΣ ADC architecture employing a time-mode loop filter is suggested in the last chapter. Several key building blocks including a time amplifier, time register and time adder for implementing such a loop filter are presented. The time amplifier has an input dynamic range of 50ps and provides a gain of 20. The implemented time register has a dynamic range of 5ns and a peak error of 2% over the 5ns full scale. The time adder remains high accuracy as long as the input time difference is no greater than 1:6ns.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".