Thermal MEMS actuator operation in aqueous media/seawater: Performance enhancement through atomic layer deposition post processing of PolyMUMPs devices
Bibliographic record
Abstract
A method to enhance thermal microelectromechanical systems (MEMS) actuators in aqueous media by using dielectric encapsulation layers is presented. Aqueous media reduces the available mechanical energy of the thermal actuator through an electrical short between actuator structures. Al2O3 and TiO2 laminates with various thicknesses were deposited on packaged PolyMUMPs devices to electrically separate the actuator from the aqueous media. Atomic layer deposition was used to form an encapsulation layer around released MEMS structures and the package. The enhancement was assessed by the increase of the elastic energy, which is proportional to the mechanical stiffness of the actuator and the displacement squared. The mechanical stiffness of the encapsulated actuators compared with the noncoated actuators was increased by factors ranging from 1.45 (for 45 nm Al2O3 + 20 nm TiO2) to 1.87 (for 90 nm Al2O3 + 40 nm TiO2). Displacement measurements were made for all laminate combinations in filtered tap water and seawater by using FFT based displacement measurement technique with a repeatability of ∼10 nm. For all laminate structures, the elastic energy increased and enhanced the actuator performance: In seawater, the mechanical output energy increased by factors ranging from 5 (for 90 nm Al2O3) to 11 (for 90 nm Al2O3 + 40 nm TiO2). The authors also measured the long-term actuator stability/reliability in seawater. Samples were stored for 29 days in seawater and tested for 17 days in seawater. Laminates with TiO2 layers allowed constant operation over the entire measurement period.
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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 | 0.001 | 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 | 0.001 |
| 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".