Plasma Treatment for Fluxless Flip-Chip Chip-Joining Process
Bibliographic record
Abstract
Flip-Chip technology is a well-established solution to increase the number of connections between a chip and a PCB. Unfortunately, with large die and high bump density, flux residues cleaning is increasingly challenging. Fluxless soldering is becoming more attractive given that flux residues cleaning step can be avoided leading to a more environment friendly process while reducing water consumption and chemical waste. Hydrogen radicals are known as a reducing agent to remove metal oxide. We present here assembly tests performed in an industrial-like environment where a hydrogen-based plasma treatment is used to suppress bumps oxide in replacement of flux chemical. The plasma treatment is performed in a vacuum capacitively coupled plasma chamber with a gas mixture containing a percentage of hydrogen. We use large 20 × 20 mm2chips and associated organic substrate, which bumps (80μm diameter and 185.6μm pitch) and pads are made of a tin-based lead-free solder. The plasma treatment is perform on both the chip and the substrate prior to assembly using furnace mass reflow. We have successfully demonstrated the assembly of several dies using a standard mass reflow furnace. In the idea of process industrialization, re-oxidation kinetic shown a process window of 48 hours between plasma treatment and chip-joining, as shown by chip-pull, optical microscopy inspection and deep thermal cycling reliability test.
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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".