Remote Actuation of Silicon Nitride Nanomechanical Resonators Using On-Chip Substrate Capacitors
Bibliographic record
Abstract
Mechanical actuation of high mechanical quality (Q) factor silicon nitride (SiN) resonators often imposes a tradeoff between integration and performance. Fully integrated electrical actuation is possible, but typically require modification of the resonators to include electrodes that can increase material damping and reduce Q-factors. Conversely, remote actuation using piezo ceramics or optical forces is bulky and typically suitable only in laboratory settings. Here we demonstrate an actuation method that does not require modifications to the resonators and that is monolithically integrated on the same chip. We show that time dependent biasing of metal-dielectric-semiconductor (MDS) capacitors on the same substrate as the resonators creates acoustic waves that can propagate towards the resonator and enable actuation without resonator modification. For a 2 V actuation signal, Ni-pSi capacitors are found to achieve 10 nm actuation amplitude in square ($1.7\times1.7$mm) low-stress (~100 MPa) SiN membrane resonators. In this case, electrical power dissipation in the chip is on the order of$0.1 ~\mu \text{W}$, and spurious heating is less than 1 mK. Both these values could be further reduced by doping the substrate to minimize resistive dissipation. First-principle models describing actuation in the charge accumulation (Ni-pSi) and charge depletion (Al-pSi) regimes are also developed. These models predict more efficient actuation using charge accumulation than charge depletion, which is confirmed experimentally. The developed actuation method is remarkably simple. In the case of Ni-pSi, it only requires attachment of wires to the chip with vacuum-compatible nickel paste, with no extra photolithography step. All the chips presented in this work are fabricated in-house, and a detailed fabrication procedure is provided. [2022-0107]
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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) | 0.002 | 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".