Characterization and Modeling of Surface Roughness on a Silicon/PZT Unimorph Cantilever using Finite Element Method
Bibliographic record
Abstract
Silicon etching using deep reactive ion etching (DRIE) at large etch depth results in rougher surfaces due to increased response in process pressure, amount of coil power, increased amount of helium leak at the backside, and even post process handling.To account for the effects of surface roughness on the characteristics of a silicon cantilever beam, a numerical model based on the finite element method (FEM) modeling was developed using actual roughness data from fabricated samples.The acquired roughness data was integrated to the silicon cantilever beam model coupled with multiphysics to simulate a piezoelectric energy harvester.Simulation result shows that roughness parameter ranging from 1.488-3.138μm can shift the resonant frequency by 5.53% to 9.48% or 308.31Hz to 551.21 Hz of the device but does not have significant effect on the output power.The significant shift in the resonant frequency implies that careful consideration of surface roughness from fabrication processes must be considered when designing energy harvesters.
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 | 0.001 | 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".