Fully automated hot embossing processes utilizing high resolution working stamps
Bibliographic record
Abstract
Nanoimprint Lithography (NIL) is a high throughput replication technology for structures ranging from micrometer down to few nanometers. NIL can be divided into UV-Nanoimprint (UV-NIL) and Hot embossing (HE). The main difference between these two techniques are the material types of both template and resist, i.e transparent templates and photosensitive resists for UV-NIL and non transparent templates and thermoplastic resists for HE. Hot embossing is a low-cost, high throughput fabrication technique of disposable, polymer based devices needed for emerging point-of care diagnostic or bio-sensing applications. This paper describes the technology for imprinting of polymer substrates as well as spin-on polymers by using soft working stamp materials on a fully automated hot embossing system, the EVGR750, built to use this rapid replication processes. Soft working stamps demonstrate the possibility to replicate both, high-aspect ratio features in thermoplastic materials as needed for microfluidic lab-on-chip applications as well as high resolution features down to 50 nm in polymers that can be used as templates for pattern transfer in the fabrication of plasmonic substrates for biosensing applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".