Design Specifications for Biaxial Navigation-Grade MEMS Accelerometers
Bibliographic record
Abstract
The focus of this paper is the design of a biaxial MEMS accelerometer for navigation applications. First, a survey is conducted to outline the commercial landscape of navigation-grade and MEMS accelerometers. The survey shows a potential market for navigation-grade accelerometers at the MEMS scale. Based on the specifications for navigation applications, the design targets are derived for the proposed biaxial MEMS accelerometers, including the common concerns of natural frequency ratios and bandwidth, as well as the important parameters for MEMS devices, such as hinge width, proof-mass size and mobility range. In light of the design targets, the ideal frequency matrix of the biaxial accelerometer system is derived based on the concept of generalized spring, in connection with the design targets. The stiffness values required are estimated herein. For further structural optimization, the parametric entries of the frequency-ratio matrix act as the objectives to be maximized for the lowest off-axis sensitivity of the proposed accelerometer. A suitable architecture for MEMS biaxial accelerometers is proposed thereafter. This architecture not only provides high compliance and structural isotropy for the in-plane translation, but also allows for direct measurement of the proof-mass motion. The proposed architecture is then optimized for the highest frequency ratio between the non-sensitive and sensitive axes, with regard to the design parameters and constraints. The optimization results of the proposed accelerometer demonstrate navigation-grade mechanical performance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".