Metallization Options for Optimum Chip-On-Board Assembly
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Chip On Board (COB) technology is in use today in a wide spectrum of applications from the very low end such as toys and hand held games (more or less throw away items with very short life cycle) to high end products such as Medical Testers, printer display panels and other similar applications. It is also one of the main enabling technologies behind the transition from Lead-frame based to organic-based packaging. With this migration the traditional boundaries of back end process and ‘strictly’ SMT assembly have become diffused – each area is learning from the other to bring greater value-add and shorter time to market for their end products. This paper examines many of the options of Metallization and Bonding for applications involving organic based substrates. In particular when COB is merged within mainstream SMT the choice of metallization has to serve both the SMT process and bonding – that choice can be a make or break factor. In the initial migration of die bonding onto organic substrates the Au/Au and Au/AlSi system was more or less defacto – ‘inherited’ from the packaging back-end. Alternative metallic systems have been known since long, but with maturity and driven by cost pressures have come under scrutiny, the options refined and made viable. The paper examines and explains in straight forward terms to the SMT professional, the strengths, limitation, pros and cons and science behind the Gold – Gold, Gold-Aluminum (AlSi & AlMg), Gold – Copper, Gold – Silver, Aluminum – Aluminum, Aluminum – Nickel and Copper – Aluminum systems. It also examines how each produces different metallurgical interactions with the various die pad and substrate metallizations, the resulting intermetallic structures and growth and how they impact reliability & cost and hence must be understood and evaluated carefully for the targeted application.
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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 | 0.001 | 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 | 0.001 | 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 it