Programmable Impulse and Random Noise Generation Circuit for Power Integrity Tests
Bibliographic record
Abstract
A novel, fully programmable noise-generation circuit is proposed for injecting random high-voltage impulses and random noise bursts, separately or combined, directly into a regulated power supply rail. It is intended as a minimally-invasive approach for assessing the impact of power supply noise on the performance of high-reliability electronic systems. The proposed circuit is made up of a high-output-current amplifier driving a pulse transformer that may be switched in or out of the DC supply path. It addresses the challenge of injecting high-voltage impulses into a regulated power rail that exhibits a very low impedance, and its small-footprint and low-cost make it suitable for System-in-Package applications requiring embedded tests. Simulation results indicates that the proposed noise source circuit successfully injects 5V random impulses of microsecond-width and 20mV random noise bursts into a high-voltage, high-current regulated power supply rail while adding very low extra power consumption.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".