Machine vision methodology for inkjet printing drop sequence generation and validation
Bibliographic record
Abstract
Abstract Inkjet printing technology for printed microelectronics suffers from a number of non-idealities due to unwanted ink flow on the substrate. This can be mitigated and pattern fidelity can be improved by using an optimized drop placement sequence in contrast to the standard raster-scanning approach. However, it is challenging to auto-generate such printing sequences for complex printed patterns. Here, the generation and evaluation of the printing sequence are turned into a computer-vision problem. The desired printed pattern is taken as an input image and converted into a printing sequence using contour, symmetric, and matrix sequencing and corner compensation. After printing, pattern defects are detected by automated image processing to evaluate the printed pattern against the designed ground truth image and to determine the best possible algorithm for printing sequence generation for different pattern types. The machine vision-based experimental approach identifies the best solutions for solving the printing and defect optimization problem in terms of precision, recall, and accuracy. This methodology will enable the automated design of electronic circuits for applications such as wearable sensors, low-cost radio-frequency identification tags, or flexible displays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".