Lateral Extensional Mode Piezoelectric ZnO-on-Nickel RF MEMS Resonators for Back-End-of-Line Integration
Bibliographic record
Abstract
High motional resistance and incompatibility with post-CMOS fabrication due to thermal budget constraints are imperative issues associated with the back-end-of-line integration of lateral extensional vibrating micromechanical resonators. This paper presents piezoelectric ZnO-on-nickel resonators as a viable means for mitigating both of the issues. Lateral extensional mode resonators equipped with thin-film piezoelectric transducers can exhibit much lower motional impedances than their capacitive counterparts due to piezo-transducers' higher electromechanical coupling coefficients. Meanwhile, the employment of electroplated nickel as the structural material allows the process temperature to be kept lower than 300 °C, which is low enough for the post-CMOS resonator fabrication. In this work, various geometrical rectangular and square plates resonators are investigated. Moreover, parallel combination of several resonators into a mechanically coupled array was explored as a systematic approach to lower motional resistance from ~1 kΩs to 0.562 kΩs. Higher order modes were investigated for achieving higher resonance frequencies up to 1.57 GHz. Local annealing by Joule heating was also exploited for quality factor improvement after device fabrication by ~2× enhancement and breaking the record of MEMS electroplated nickel resonators in lowering insertion loss to ~10 dB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".