Minimizing Stiction of Microelectromechanical Systems (MEMS) Through Solution-Phase Surface Modification with Alcohols
Bibliographic record
Abstract
This article demonstrates implementation of solutionphased formation of alcohol based monolayers as a means to minimize stiction of microelectromechanical systems (MEMS). Surface modifications to a variety of metal and semiconductors have been attempted to reduce mechanical stiction of overhanging structures in MEMS devices. Issues associated with stiction arise from either the release of sacrificial layers during fabrication processes (i.e., release stiction), or the interactions of surfaces during subsequent device operation (i.e., in-use stiction). To address both release and in-use stiction, we propose a new approach that enables the release of sacrificial structures and formation of organic based monolayers on microscale structures. In this approach, unlike traditional approaches, the entire process is performed in solution-phase, and the structure(s) remain in solution until they are fully coated with covalently attached alcohol based monolayers. This approach minimizes the release stiction caused by surface tension during drying processes. The approach is also easily scalable and implementable in laboratory or industrial settings since it utilizes widely prevalent solvents that can be easily accessed and handled without the need for meticulous control over environmental humidity.
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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) | 0.000 | 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 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".