Heterogeneous MEMS device assembly and integration
Bibliographic record
Abstract
In recent years, smart phone applications have both raised the pressure for cost and time to market reduction, and the need for high performance MEMS devices. This trend has led the MEMS community to develop multi-die packaging of different functionalities or multi-technology (i.e. wafer) approaches to fabricate and assemble devices respectively. This paper reports on the fabrication, assembly and packaging at INO of various MEMS devices using heterogeneous assembly at chip and package-level. First, the performance of a giant (e.g. about 3 mm in diameter), electrostatically actuated beam steering mirror is presented. It can be rotated about two perpendicular axes to steer an optical beam within an angular cone of up to 60° in vector scan mode with an angular resolution of 1 mrad and a response time of 300 ms. To achieve such angular performance relative to mirror size, the microassembly was performed from sub-components fabricated from 4 different wafers. To combine infrared detection with inertial sensing, an electroplated proof mass was flip-chipped onto a 256×1 pixel uncooled bolometric FPA and released using laser ablation. In addition to the microassembly technology, performance results of packaged devices are presented. Finally, to simulate a 3072×3 pixel uncooled detector for cloud and fire imaging in mid and long-wave IR, the staggered assembly of six 512×3 pixel FPAs with a less than 50 micron pixel co-registration is reported.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".