Glass Imprint Templates by Spark Assisted Chemical Engraving for Microfabrication by Hot Embossing
Bibliographic record
Abstract
As the field of microelectromechanical systems (MEMS) matures, new demands are being placed on the microfabrication of complex architectures in robust materials, such as hard plastics. Iterative design optimization in a timely manner—rapid prototyping—places challenges on template fabrication, for methods such as injection moulding and hot embossing. In this paper, we demonstrate the possibility of using spark assisted chemical engraving (SACE) to produce micro patterned glass templates. The direct, write-based approach enabled the facile fabrication of smooth microfeatures with variations in all three-dimensions, which could be replicated by hot embossing different thermoplastics. As a proof of principle, we demonstrated the technique for a high glass transition temperature polycarbonate. Good fidelity over more than 10 cycles provides evidence that the approach is viable for rapid prototyping and has the potential to satisfy commercial-grade production at medium-level output volumes. Glass imprint templates showed no degradation after use, but care must be taken due to brittleness. The technique has the potential to advance microfabrication needs in academia and could be used by MEMS product developers.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".