Design for Manufacturability a Holistic Approach for the Development of MEMS Inertial Sensors
Bibliographic record
Abstract
Micro-electromechanical system (MEMS) inertial sensors aiming for a high accuracy must anticipate and accommodate for manufacturing variations during the design stage, thereby minimizing reliance on post-fabrication active or passive mitigation measures. In this article, we present a prefabrication design approach that utilizes historical manufacturing data to fit models for fabrication process simulation to predict fabricated device geometry, incorporating account for potential variations in fabrication. Leveraging analytical and finite element modeling, we recreate the manufactured device and its operating conditions to predict its performance. We systematically gathered and analyzed data from hundreds of historical SEM images depicting various trench widths ranging from 1 to$50~\mu $m and depths from 10 to$70~\mu $m, and fit models to predict key fabrication variations, such as undercut and taper angle. Subsequently, the predicted manufactured device was imported into finite element analysis (FEA), enabling us to anticipate the resonance frequency range of our model while accounting for manufacturing inaccuracies and various damping. Our approach predicted a resonance frequency of 469 Hz and a Q-factor of air 42.6 for the manufacture variation included model (MVIM), aligning closely with experimental findings at 460 Hz and a Q-factor of air 41.8. In contrast, the simulated resonance frequency without considering any fabrication variations was 538 Hz for the designed model (DM). This innovative design for manufacturing shows the significance of incorporating fabrication variations and operating conditions into the simulation stage, aiding the further development of MEMS sensors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".