Microfabrication services at INO
Bibliographic record
Abstract
MEMS (Micro Electro Mechanical Systems) technology has expanded widely over the last decade in terms of its use in devices and instrumentation for diverse applications. However, access to versatile foundry services for MEMS fabrication is still limited. At INO, the presence of a multidisciplinary team and a complete tool set allow us to offer unique MEMS foundry-type services. These services include: design, prototyping, fabrication, packaging and testing of various MEMS and MOEMS devices. The design of a device starts with the evaluation of different structures adapted to a given application. Computer simulation tools, like IntelliSuite, ANSYS or custom software are used to evaluate the mechanical, optical, thermal and electromechanical performances. Standard IC manufacturing techniques such as metal, dielectric and semiconductor film deposition and etching as well as photolithographic pattern transfer are available. In addition, some unique techniques such as on-wafer lithography by laser writing, gray-scale mask lithography, thick photoresist lithography, selective electroplating, injection moulding and UV-assisted moulding are available to customers. The hermetic packaging and a novel patented wafer-level micropackaging are also applied. This multifaceted expertise has been utilized to manufacturing of several types of MEMS devices as well as complex instruments including micromirror-type devices, microfilters, IR microbolometric detector arrays, complete cameras and multipurpose sensors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.418 | 0.288 |
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".