Wire Bonding UPH and Stitch Bond Improvement using 20 Micron Diameter Insulated Wire with Security Bump
Bibliographic record
Abstract
In this study, security bumps are used for strengthening the stitch bonds of two 20 micron diameter insulated Au wire bonding example processes. Bump bonding as a variant of the ball bonding process has been commonly used in the microelectronic industry to make bumps on dies that will later be flip-chip bonded. The optimized stitch bond parameters combined with the security bumps placed upon the stitch bonds substantially improve the second bond strength demonstrated on the two example processes on two different types of wire bonding equipment. A comparison of pull test results shows that security bumps increase stitch pull force up to 100%. The effect of varying the relative position (shift) of the security bump relative to the stitch bond location is investigated for one process. The window with the highest pull force improvement is ranging from 16 to 31 micron shift towards the ball bond. Looping with insulated wire is faster than with bare wire because of less effort to mitigate the risks of wires touching each other and producing a short. If two wire loops touch each other e.g. after molding, the wire insulation prevents shorts. Therefore, the looping requirements of the example processes with security bumps can be relaxed by reducing the number of kinks (reverses) from four to two. Due to the reduced looping complexity, the overall UPH increased with insulated wire by about 3.0 % and 4.9 % for the two processes, respectively. This increase is in spite of the time required for the additional security bumps, and compared to bare wire processes without security bumps but with more complex looping.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".