A nonlinear stiffness softening mechanism for low-bias, high-sensitivity MEMS accelerometers with extended dynamic range
Bibliographic record
Abstract
The small dimensions of microfabricated devices present challenges in applications such as inertial sensing, where a larger proofmass is necessary for enhanced sensitivity. An effective approach to addressing the limitations of linear sensing is to use nonlinear mechanisms that adapt the device’s response according to different operating conditions. This paper introduces a new nonlinear spring mechanism for use in microsensors that harnesses the buckling phenomenon to achieve stiffness softening. The proposed mechanism utilizes a micro-arm to apply an eccentric axial load to an inclined beam, causing it to buckle in a controlled manner under a specified load. Once buckled, linear springs dominate the response of the system. We demonstrate that this method results in a smaller bias displacement compared to previously reported techniques based on snap-through behaviour, leading to potential reductions in device size and improvements in operational range. The behaviour is analytically modelled and verified through simulations. A prototype device was designed and microfabricated to experimentally validate the design principles. Compared to pre-curved nonlinear springs, the proposed design results in an 11-fold reduction in bias force, a 100-fold reduction in bias displacement, and a reduction in mechanical stiffness by a factor of 520. These results were verified through experiments conducted on a microfabricated accelerometer with an on-chip optical interferometer. Test results reveal an extended linear range of better than $$150\,\mathrm{mg}$$ , a bias force of 0.3 $$\mathrm{mN}$$ , and a bias displacement of 10 $$\mathrm{\mu m}$$ , measured with an integrated optical interferometer with a displacement noise floor of 40 $$\mathrm{pm}/\sqrt{\mathrm{Hz}}$$ at 2 $$\mathrm{Hz}$$ and sensitivity of $${194}^{\circ }/\mathrm{mg}$$ .
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".