Fast yield estimation and optimization of microwave filters using a cognition-driven formulation of space mapping
Why this work is in the frame
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Bibliographic record
Abstract
A cognition-driven formulation of space mapping (SM) is effective for equal-ripple optimization of microwave filter. In this paper, we use cognition-driven SM to estimate yield in the design of microwave filters. With mappings from the statistical variable space to feature parameter spaces, we can find the distribution of these intermediate feature parameters with respect to the statistical variables. A correction method is proposed to improve the accuracy of the mappings. Thus, we can determine the yield by checking whether the ripple height parameters and some specific feature frequency parameters satisfy the specifications or not. The number of EM simulations of the proposed yield estimation method is linear with respect to the number of statistical variables. We further propose a yield optimization method using our yield estimation. Our method is verified using a waveguide filter and Monte Carlo analysis.
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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 | 0.000 |
| Open science | 0.000 | 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