BAW temperature sensitivity and coupling in langanite
Bibliographic record
Abstract
One of the new materials belonging to the trigonal class 32, to which quartz belongs, is langanite (LGN, La3Ga5.5Nb0.5O14). High-quality LGN single crystals are now available, and, although similar in composition and structure to langasite (LGS, La3Ga5SiO14), LGN has smaller thermal expansion coefficients and comparable piezoelectric constants to LGS. These are desirable material properties for both SAW and BAW applications that require low frequency dependence on temperature. This paper examines in detail the LGN characteristics: phase velocity, temperature coefficient of frequency (TCF), electromechanical coupling coefficient, and power flow angle for both singly and doubly rotated plate cuts. Contour plots of these characteristics are constructed, revealing orientation regions where zero TCF and high coupling exist and suggesting potentially interesting cuts for practical BAW device design. Temperature compensated cut regions with coupling coefficients as high as 0.16 are predicted, which is twice the value for AT-cut quartz, along with a temperature compensated cut with cubic behavior around room temperature for one of the sets of material constants used. With such desirable properties, LGN is a promising candidate material for BAW applications requiring low temperature sensitivity with superior bandwidth characteristics due to its values of coupling coefficient larger than quartz. Several other orientations with low TCF and high coupling are also identified.
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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 | 0.000 |
| 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) | 0.001 | 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".