A Cross-Channel Parametric CNN Framework for Microwave Topology Modeling
Bibliographic record
Abstract
Topology design has emerged as an efficient approach in the domain of electromagnetic (EM) design. Despite extensive research, accurately modeling high-performance topology structures remains a formidable challenge. This article proposes an advanced convolutional neural network (CNN) architecture, named cross-channel parametric small convolution network (CCP-SConvNet), tailored to microwave topology modeling. A CCP pooling mechanism combined with multiple small convolutional filters is developed to facilitate fine-grained spatial abstraction and adaptive interchannel information fusion. As a result, CCP-SConvNet improves predictive accuracy and robustness while reducing the overall parameter count compared to traditional CNNs. To ensure fabrication feasibility, a modified breadth-first search (BFS) algorithm is proposed to eliminate nonphysical structures such as suspended metal elements, thereby reducing the volume of training data required for model development. By integrating dynamic data generation with joint optimization strategies, the proposed framework further alleviates dependence on large datasets and accelerates the overall design process. CCP-SConvNet thus offers a rapid and cost-effective alternative to full-wave EM simulations, enabling efficient modeling and optimization of microwave topological components. The efficacy and robustness of the proposed CCP-SConvNet are validated through three representative cases: a single-pole waveguide filter operating at 11–13 GHz with a prediction error of 3.56%, a three-pole waveguide filter at 7.4–8.8GHz with an error of 2.82%, and a microstrip line filter at 1.2–2.8GHz with an error of 3.36%. The model reduces the required training data by up to 90% through the modified BFS strategy and accelerates EM simulation from minutes to milliseconds. The single-pole filter was fabricated and experimentally measured, exhibiting good agreement between the measured and predicted$S$-parameters. These results demonstrate the superior capability of CCP-SConvNet in accurate, efficient, and physically validated modeling of advanced microwave topological components.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".