Alternative nano-structured thin-film materials used as durable thermal nanoimprint lithography templates
Bibliographic record
Abstract
Nanoimprint templates made of diamond-like carbon (DLC) and amorphous silicon carbide (SiC) thin films and fluorine-doped associated materials, i.e. F-DLC and F-SiC were investigated in the context of thermal nanoimprint lithography (NIL) with respect to their release properties. Their performances in terms of durability and stability were evaluated and compared to those of conventional silicon or silica molds coated with antisticking molecules applied as a self-assembled monolayer. Plasma-enhanced chemical vapor deposition parameters were firstly tuned to optimize mechanical and structural properties of the DLC and SiC thin films. The impact of the amount of fluorine dopant on the deposited thin films properties was then analyzed. A comparative analysis of DLC, F-DLC as well as SiC and F-SiC molds was then carried out over multiple imprints, performed into poly (methyl methacrylate) (PMMA) thermo-plastic resist. The release properties of un-patterned films were evaluated by the measurement of demolding energies and surface energies, associated with a systematic analysis of the mold surface contamination. These analyses showed that the developed materials behave as intrinsically easy-demolding and contamination-free molds over series of up to 40 imprints. To our knowledge, it is the first time that such a large number of imprints has been considered within an exhaustive comparative study of materials for NIL. Finally, the developed materials went through standard e-beam lithography and plasma etching processes to obtain nanoscale-patterned templates. The replicas of those patterned molds, imprinted into PMMA, were shown to be of high fidelity and good stability after several imprints.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".