STOCHASTIC ADC WITH RANDOM U-QUADRATIC DISTRIBUTED REFERENCE VOLTAGES TO UNIFORMLY DISTRIBUTE COMPARATORS TRIP POINTS
Bibliographic record
Abstract
This thesis presents a new architecture of stochastic Analog-to-Digital converter (ADC). A standard Stochastic ADC uses comparator random offset as the trip point while all the comparators have the same reference voltages. Since the offset of a basic comparator depends on a number of independent random variables, the offset will follow randomly distributed Gaussian function. The input dynamic range of this standard stochastic ADC is ±?. For 90nm technology ? value is around 153mV. A technique is presented that converts overall transfer function of a stochastic ADC i.e. Gaussian distribution into almost uniformly distribution with a wider range. With the proposed technique, an input dynamic range of ± 153mV and ENOB of 4bits of standard stochastic ADC are increased to variable input dynamic range of ±250mV to ±500mV and ENOB of 6bits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".