Multistage anisotropic wet etching by KOH for MEMS/NEMS structures
Bibliographic record
Abstract
Adaptive Optics (AO) systems add value in applications that involve light collection through turbulent media. Examples include Earth-based telescopes, microscopy, optical communication, and high-energy lasers, where solutions for addressing distorted images are needed. A key challenge in building AO systems involves achieving sufficient correction of distorted images through optical elements that can change shape with enough vertical displacement, spatial resolution, and frequency to adequately correct images. One such AO element is a deformable mirror that is bonded to an array of MEMs-based actuators that is made to operate using the Lorentz force. These elements can operate with very low power and are known as Low-Voltage Deformable Mirrors (LVDM). This study delves into a multi-step Potassium Hydroxide (KOH) etching process, encompassing surface preparation, mask deposition, lithography, and etching. KOH bulk micromachining proves critical in precision crafting of detailed 3D microstructures through anisotropic wet etching, offering unparalleled control over structural depth and morphology. LVDM Lorentz actuators are systematically arrayed in 20 × 20 geometries, interconnected with a flexible, reflective membrane mirror. Fabrication of the actuator arrays using Silicon on Insulator (SOI) wafers utilizes a multi-step KOH bulk process, with a design that integrates a crossbar, pillar, and springs, with a need for a crucial depth of up to 35μm. This study provides a detailed account of the two-step KOH etching process, emphasizing critical parameters for successful Lorentz actuator fabrication on SOI wafers. Exploration of process variables like temperature, etchant concentration, and etching time is undertaken, considering their impact on etch rate and selectivity. Understanding these variables proves vital for achieving high-quality and reproducible MEMS structures in micro-/nanofabrication. The study also tackles challenges associated with multistage anisotropic wet etching, including etch pit formation, crystallographic defects, and surface roughness. Strategies to mitigate these challenges, such as surfactant use, additives, and post-etch treatments, are discussed, emphasizing their effectiveness in improving final device performance and reliability.
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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".