Hybrid wafer-level vacuum hermetic micropackaging technology for MOEMS-MEMS
Bibliographic record
Abstract
Packaging constitutes one of the most costly steps of MEMS/MOEMS manufacturing. The package protects the MEMS devices and, in the case of MOEMS, it also provides light access to the device. In many cases, MEMS require a specific atmosphere for their proper functioning. The atmosphere should be kept invariable during the lifetime of the package in order to not degrade the performance of the device. Maintaining a constant atmosphere inside the package becomes more challenging as the cavity volume is decreased to the microliter and nanoliter range. Other packaging requirements are compatibility with wafer-level microfabrication techniques (cost reduction) and low temperature assembly in cases where temperature sensitive devices are to be packaged. In recent years, INO has performed a great amount of work towards the development of uncooled IR microbolometer detectors using VOx technology. Different pixel designs have been optimized for different applications. The bolometer pixels require a vacuum atmosphere below 10 mTorr to be maintained during the lifetime of the device in order to operate at their highest sensitivity. INO's micropackaging technology has been demonstrated to provide base pressures below 5 mTorr. An equivalent flow rate of 2.5×10-14 Torr.l/sec has been obtained for a device packaged without any getter. The advantages of INO's micropackaging technology are the possibility of achieving very low base pressures, the low temperatures required for the assembly (the package device is never exposed to a temperature above 150 °C) and its compatibility with hybrid wafer-level packaging. The technology has been developed for the micropackaging of INO's 160×120 pixel uncooled microbolometer FPA, but it is compatible with any other kinds of MOEMS-MEMS devices requiring vacuum hermetic packaging. In order to increase the lifetime of the package, knowledge of the gases outgassing inside the package is crucial. A hybrid approach has been chosen as it permits packaging only known-good dies and saving considerable quantities of IR window material. In INO's hybrid wafer-level packaging, dicing is performed only through one of the wafers, therefore reducing the risk of perturbing the vacuum during the separation of the different dies.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".