Roughness characterisation of gas phase micromachining process suitable for fabricating silicon based microsystems
Bibliographic record
Abstract
Non-conventional or advanced machining techniques are becoming the enabling fabrication techniques for many emerging fields including Micro Electro Mechanical Systems (MEMS) or Microsystems Technology (MST). The processes used for MEMS fabrication include standard semiconductor fabrication processes and emerging micromachining techniques. Among the challenges emerging from the manufacturing of MEMS devices, post-processing seems to be one of the most sensitive issues. The non-traditional common post-processing techniques are bulk micromachining and surface micromachining. It has been a challenge for MEMS designers to develop a micromachining technique that is compatible with IC (Integrated Circuits) processes and also capable of making MEMS structures through both bulk and surface micromachining with acceptable surface roughness requirements. The selected micromachining process should not affect the integrity of the free standing structure due to the reduced selectivity and aggressive etch of the adjacent electronic circuitry. Moreover, the integrity of the released structure, the dynamic properties as well as the electrostatic characteristics, are strongly dependent on the achieved roughness of the surfaces produced by the etching process. Hence, this paper presents the surface roughness characterisation of gas phase micromachining with XeF2 that is suitable for fabricating integrated MEMS with both micromechanical and microelectronics components. This paper also presents some fabricated microsystems using this process.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".