Comparison of Sintering Methodologies for 3D Printed High-Density Interconnects (2.3 L/S) on Organic Substrates for High-Performance Computing Applications
Bibliographic record
Abstract
High performance computational demands are pushing for rapid technological advancements in the field of electronics and packaging. The high-density organic interposer has emerged as a cost-effective alternative to the conventional silicon interposer. Using redistribution layers (RDLs) can help in optimization of heterogenous packages, while providing significant benefits to signal integrity. The challenge, however, lies in dimensional limitations imposed by the rough organic substrate on established fabrication approaches for the RDLs. This paper explores the use of additive manufacturing as an alternative technique to fabricate high-density interconnects on non- conductive organic substrates. Printing of interconnects with line/space of 2.3/2.5 using silver inks is developed and demonstrated directly on organic substrates coated with a dielectric film. Sintering of the nanoparticle ink is a critical step to obtaining high-resolution conductive patterns. Conventional thermal oven sintering is not compatible with our application, given the low glass transition temperatures of the substrate and the dielectric film. We, therefore, investigate photonic curing as a novel and alternative sintering technology for high-resolution printed structures and compare it to the thermal oven curing method. Resistivity values obtained are almost twice that of the thermal oven cure for the ink used. A final comparison is made where single step photonic curing was observed to produce a lower resistivity value as compared to a combination of thermal and photonic curing for connector widths < 5
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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 | 0.001 | 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) | 0.001 | 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".