Low-Stress Thermosonic Copper Ball Bonding
Bibliographic record
Abstract
Thermosonic ball bonding processes on test chips with Al metallized bonding pads are optimized with one Au and two Cu wire types, all 25 mum diameter, obtaining average shear strengths of more than 120 MPa. The process temperature is ~110degC. Ball bonds made with Cu wire show at least 15% higher shear strength than those made with Au wire. The estimated maximum shear strength cpkvalue determined for Cu ball bonding (cpk= 3.7 plusmn 1.2) is almost 1.5 times as large as that of the Au ball bonding process (cpk= 2.3 plusmn 0.9), where LSL is 65.2 MPa. However, the ultrasound level required for Cu is approximately 1.3 times than that required for Au. Consequently, about 30% higher ultrasonic forces induced to the bonding pad are measured using integrated real-time microsensors. The accompanying higher stresses increase the risk of under-pad damage. One way to reduce ultrasonic bonding stresses is by choosing the softer of the two Cu wire types, resulting in a measured ultrasonic force reduction of about 5%. A second way is to reduce the ultrasound level. While this causes the average shear strength to fall by 15%, the ultrasonic force falls by 9%. The cpkvalue does not change significantly, suggesting that a successful Cu ball bonding operation can be run with about 0.9 times the conventionally optimized ultrasound level. The process adjusted in this way reduces the extra stress observed with Cu wire compared to that observed with Au wire by 42%.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".