Printing accuracy tracking with 2D optical microscopy and super-resolution metamaterial-assisted 1D terahertz spectroscopy
Bibliographic record
Abstract
Abstract Printable electronics is a promising manufacturing technology for the potential production of low-cost flexible electronic devices, ranging from displays to active wear. It is known that rapid printing of conductive ink on a flexible substrate is vulnerable to several sources of variation during the manufacturing process. However, this process is still not being subjected to a quality control method that is both non-invasive and in situ. To address this issue, we propose controlling the printing accuracy by monitoring the spatial distribution of the deposited ink using terahertz (THz) waves. The parameters studied are the printing speed of an industrial roll-to-roll press with flexography printing units and the pre-calibration compression, or expansion factor, for a pattern printed on a flexible plastic substrate. The pattern, which is carefully selected, has Babinet’s electromagnetic transmission properties in the THz frequency range. To validate our choice, we quantified the geometric variations of the printed pattern by visible microscopy and compared its accuracy using one-dimensional THz spectroscopy. Our study shows a remarkable agreement between visible microscopic observation of the printing performance and the signature of the THz transmission. Notably, under specific conditions, one-dimensional (1D) THz information from a resonant pattern can be more accurate than two-dimensional (2D) microscopy information. This result paves the way for a simple strategy for non-invasive and contactless in situ monitoring of printable electronics production.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".