Adaptively Weighted Yield-Driven EM Optimization Incorporating Neurotransfer Function Surrogate With Applications to Microwave Filters
Bibliographic record
Abstract
In this article, we propose an adaptively weighted yield-driven EM optimization technique incorporating neurotransfer function (neuro-TF) surrogate. In the proposed technique, an adaptive weighting factor is set up for each frequency point of interest based on the degree to which the EM response may violate the design specification. These weighting factors are first incorporated into the error function for training the neuro-TF model and then involved in the objective function of yield optimization using the trained model. We identify the key frequency points where the EM response is likely to violate the design specification over the whole frequency range of interest. Using the adaptive weighting factor-incorporated error function to train the model enhances the model accuracy at the key frequency points while preserving the model accuracy at other ordinary frequency points. This improves the yield estimation accuracy using the trained surrogate model at each iteration of optimization and, consequently, facilitates the yield optimization process. By involving the weighting factors into the formulation of the objective function of neuro-TF-assisted yield optimization, higher priorities are given to the key frequency points than the ordinary frequency points. This allows the proposed technique to find a more effective update direction at each iteration of optimization and, consequently, achieves a similar yield increase with a fewer number of iterations compared with the conventional neuro-TF approach. Two microwave examples demonstrate the advantages of the proposed technique against other existing approaches, including the Monte Carlo (MC)-based approach, the polynomial chaos (PC)-based approach, and the conventional neuro-TF approach.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".