Bibliographic record
Abstract
As thermosonic ball bonding is developed for more and more advanced applications in the electronic packaging industry, the control of process stresses induced on the integrated circuits becomes more important. If Cu bonding wire is used instead of Au wire, larger ultrasonic levels are common during bonding. For advanced microchips the use of Cu based wire is risky because the ultrasonic stresses can cause chip damage. This risk needs to be managed by e.g. the use of ultrasound during the impact stage of the ball on the pad ("pre-bleed") as it can reduce the strain hardening effect, which leads to a softer deformed ball that can be bonded with less ultrasound. To find the best profiles of ultrasound during impact, a numerical model is reported for ultrasonic bonding with capillary dynamics combined with a geometrical model describing ball deformation based on volume conservation and stress balance. This leads to an efficient procedure of ball bond modelling bypassing plasticity and contact pairs. The ultrasonic force and average stress at the bond zone are extracted from the numerical experiments for a $50{\mu}m$ diameter free air ball deformed by a capillary with a hole diameter of $35{\mu}m$ at the tip, a chamfer diameter of $51{\mu}m$ , a chamfer angle of $90^{\circ}$ , and a face angle of $1^{\circ}$ . An upper limit of the ultrasonic amplitude during impact is derived below which the ultrasonic shear stress at the interface is not higher than 120 MPa, which can be recommended for low stress bonding.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".