Low-Roughness UV Laser Drilling for Sub-10 μm Vias in Advanced Semiconductor Package Substrates
Bibliographic record
Abstract
Conventional CO2laser drilling and wet desmearing processes have limitations in forming fine pitch microvias, particularly in advanced microcircuit applications. Although ultraviolet (UV) laser drilling can produce smaller via diameters, its use is limited for buildup films (BUF) protected by polyethylene terephthalate (PET). Direct plasma exposure of the BUF can cause filler protrusion and increase surface roughness. This, in turn, necessitates thicker seed layers in the semi-additive process (SAP) and ultimately hinders the realization of fine circuit patterns. To solve this problem, we evaluated the effect of material and thickness of SMBL (Sacrificial Metal Barrier Layer) on laser via hole formation before nanosecond UV laser drilling. Two SMBL materials, Cu and Ni-Cr, were coated on a 10 μm thick buildup film (BUF) surface at thicknesses of 50 nm, 100 nm, and 200 nm, then fine vias were processed with a nanosecond UV laser and their shapes were comparatively evaluated. As a result, using a nanosecond UV laser drilling system with a sputter-coated Cu SMBL, we were able to form microvias with diameters of less than 8 μm on a 10 μm thick BUF surface while maintaining the original surface roughness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".