Examination of Key Packaging Metrics of a Hermetically Sealed MEMS Accelerometer
Bibliographic record
Abstract
ABSTRACT Micralyne Inc. has developed a high performance MEMS accelerometer that makes use of wafer level interconnection and packaging. This paper shares performance metrics for a selection of enabling technologies that allow these sensors to achieve ultrahigh sensitivity. Processing strategies and metrology techniques that have been developed through the course of the product development lifecycle are explored. Achieving low-noise in this product requires that the proof mass is encapsulated within an evacuated cavity. This encapsulation process must achieve a cavity pressure less than of 0.5 Pa over the duration of the lifetime of the device. This specification requires leak rates less than 1·10 −17 Pa·m 3 ·s −1 on every die during the hermetic sealing process. Such leak rates are not detectable using published testing protocols employing helium bombing. Micralyne has developed a protocol to accurately measure these leak rates and has developed a hermetic sealing protocol using Au-Si eutectic bonding that repeatedly meets this specification. The hermetic seal’s high performance must not be compromised by other features of the device, namely the electrical interconnections required to route electrical signals in and out of the device while maintaining low noise and distortion for industrial applications. Micralyne has developed a patent protected process that suitably routes these electrical signals from the device layer of a cavity silicon on insulator wafer to the substrate backside. This routing is achieved using an oxide-lined through-silicon-via (TSV) filled with conductive polysilicon. The resulting substrate is suitable for chip stacking as the signals are all routed to the reverse of the substrate. Micralyne is currently employing this sensor platform, which it calls μSilQ™ (pronounced Micra-silk), with the MEMS chip directly attached to a board or ASIC using a solder ball-grid-array (BGA) placed on the wafer backside prior to singulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".