The numerical analysis of general SAW and leaky wave devices using approximate Green's function representations
Bibliographic record
Abstract
The Green's function or boundary element method (BEM) is the best available technique for rigorous surface acoustic wave (SAW) device analysis. However, its computational cost usually means that it cannot be applied directly to devices with complex, nonperiodic electrode structures. In this paper, approximate forms for the Green's function are employed. They are based on rigorous representations, they can represent the Green's function to any required degree of accuracy, and they can be applied to any type of substrate and acoustic wave. The use of this type of approximation for practical device analysis is considered, and computational procedures are presented that can exploit the special approximate Green's function structure. It is shown that highly efficient computational algorithms can be constructed, in which the computational effort increases linearly with the number of electrodes in the device. These methods can be applied to any type of device structure, and they do not require any empirically derived parameters. The practical application of the methods is illustrated by examples of longitudinally coupled resonator filter (LCRF) designs implemented using leaky wave cuts of lithium tantalate. Agreement between theory and experiment is excellent, even for devices of this complexity.
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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".