Aerosol‐Jet Printed Fillets for Well‐Formed Electrical Connections between Different Leveled Surfaces
Bibliographic record
Abstract
Abstract As additive manufacture becomes more prevalent in the fabrication of advanced electronics, there is a need to create well‐formed, robust circuitization, and interconnects between components mounted onto different leveled surfaces (DLSs). Here, an algorithm is developed for aerosol‐jet printing of fillet structures that enable such a circuitization and hence a smooth electrical transition between the DLSs. The fillets are printed using an ultraviolet‐curable polymer ink in the presence of in situ curing. A specific deposition rate is established in order to ensure a precise architecture. Further, a surface smoothing technique is employed to smooth out the stepped surface topology of a fillet resulting from the layer‐by‐layer printing of in situ cured material. Finally, it is ascertained that the performance of these printed fillets is highly satisfactory by carrying out the resistance measurements of the conducting lines printed over these fillet structures both before and after temperature cycling and establishing the mechanical stability of the fillets by employing an adhesion test. This technology ensures that the fillets not only establish a mechanical integration/attachment of the two DLSs, but more importantly that they also provide a well‐formed surface onto which an electrical connection between these two DLSs can be established.
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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) | 0.002 | 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".