A Predictable Robust Fully Programmable Analog Gaussian Noise Source for Mixed-Signal/Digital ATE
Bibliographic record
Abstract
A robust programmable analog Gaussian noise generator suitable for mixed-signal/digital ATEs is presented. Unlike conventional methods (LFSR based noise generators or resistor thermal noise amplification techniques), the user has full control of the characteristics of the Gaussian signal. Indeed, the frequency band, the mean, and variance of the distribution are fully programmable over the voltage range within the supply rails. The method consists of digitally encoding the specified Gaussian signal in a RAM, using pulse-density modulation, followed by filtering the bit stream using an analog low-pass filter. It is demonstrated that the quality of the generated noise signal is independent of the quality of the filter used; hence, making the noise source highly robust. The output of the noise generator accurately models a real Gaussian signal, even at high sigma values; thus, making it a very effective and predictable dithering signal. Two applications of the proposed Gaussian noise source are demonstrated: ADC histogram testing and high-resolution digitization
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".