Solving the shrinkage-induced PDMS alignment registration issue in multilayer soft lithography
Bibliographic record
Abstract
Shrinkage of polydimethylsiloxane (PDMS) complicates alignment registration between layers during multilayer soft lithography fabrication. This often hinders the development of large-scale microfabricated arrayed devices. Here we report a rapid method to construct large-area, multilayered devices with stringent alignment requirements. This technique, which exploits a previously unrecognized aspect of sandwich mold fabrication, improves device yield, enables highly accurate alignment over large areas of multilayered devices and does not require strict regulation of fabrication conditions or extensive calibration processes. To demonstrate this technique, a microfabricated Braille display was developed and characterized. High device yield and accurate alignment within 15 µm were achieved over three layers for an array of 108 Braille units spread over a 6.5 cm 2 area, demonstrating the fabrication of well-aligned devices with greater ease and efficiency than previously possible.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".