Optimizing an LDO voltage regulator by evolutionary algorithms considering tolerances of the circuit elements
Why this work is in the frame
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Bibliographic record
Abstract
A low-dropout (LDO) voltage regulator is optimized by evolutionary algorithms. Basically, tolerance analysis is performed alike a worst-case analysis within SPICE to rank the circuit elements presenting higher sensitivities, and according to the target specifications associated to two objectives, namely: Power Supply Rejection (PSR) and output capacitor value. The results from the tolerance analysis are used to propose for the first time a chromosome of the LDO to perform multiobjective optimization by evolutionary algorithms. In addition, the computed tolerances are used to establish reduced search spaces for the circuit elements included into the chromosome, so that the optimization by applying the non-dominated sorting genetic algorithm (NSGA-II) being accelerated. As a result, the main contribution is the application of tolerance analysis to set the chromosome that includes few circuit elements to optimize an LDO voltage regulator, and to establish reduced search spaces to accelerate computing time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it