Adaptively Weighted Training of Space-Mapping Surrogates for Accurate Yield Estimation of Microwave Components
Bibliographic record
Abstract
Electromagnetic (EM)-based yield estimation plays an important role in microwave design due to the presence of uncertainties in manufacturing processes. In this paper, we propose a novel training approach with adaptive weighting factors to increase the yield estimation accuracy of microwave components using space mapping (SM) surrogates. In this approach, an adaptive weighting factor is set up for each frequency point of interest based on the sensitivity degree of the EM response relative to the design specification. A novel error function incorporating the adaptive weighting factors is proposed specifically for EM-based yield estimation. Using the proposed error function to train the SM surrogate enhances the model accuracy at the key frequency points where the EM response is sensitive w.r.t. to statistical variables while preserving the model accuracy at other ordinary frequency points over the whole frequency range of interest. Compared with the existing training method, the proposed approach achieves higher yield estimation accuracy especially for microwave circuits with high sensitivities. The proposed approach is illustrated by a microwave filter example.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".