Rapid Yield Estimation of Microwave Passive Components Using Model-Order Reduction Based Neuro-Transfer Function Models
Why this work is in the frame
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Bibliographic record
Abstract
In this letter, we propose a novel technique for rapid and accurate yield estimation of microwave passive components using model-order reduction (MOR)-based neuro-transfer function (neuro-TF) models. In the proposed technique, the frequency responses of microwave components are represented by transfer functions in the pole-zero-gain format. The poles, zeros, and gain in the transfer functions are computed by the MOR technique. Neural networks are trained to capture the dynamic changes of the poles/zeros/gain as the statistical/geometrical variables change. A refinement training process is designed to further align the outputs of the neuro-TF model. Once developed, the MOR-based neuro-TF model can provide rapid and accurate prediction of electromagnetic (EM) behavior of microwave passive components, thereby accelerating EM-based yield estimation. To achieve similar yield estimation accuracy, the proposed technique requires a shorter CPU time than existing yield estimation methods. The advantages of the proposed technique are illustrated by two microwave examples.
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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