Ultrasonic friction power during Al wire wedge-wedge bonding
Bibliographic record
Abstract
Al wire bonding, also called ultrasonic wedge-wedge bonding, is a microwelding process used extensively in the microelectronics industry for interconnections to integrated circuits. The bonding wire used is a 25μm diameter AlSi1 wire. A friction power model is used to derive the ultrasonic friction power during Al wire bonding. Auxiliary measurements include the current delivered to the ultrasonic transducer, the vibration amplitude of the bonding tool tip in free air, and the ultrasonic force acting on the bonding pad during the bond process. The ultrasonic force measurement is like a signature of the bond as it allows for a detailed insight into mechanisms during various phases of the process. It is measured using piezoresistive force microsensors integrated close to the Al bonding pad (Al–Al process) on a custom made test chip. A clear break-off in the force signal is observed, which is followed by a relatively constant force for a short duration. A large second harmonic content is observed, describing a nonsymmetric deviation of the signal wave form from the sinusoidal shape. This deviation might be due to the reduced geometrical symmetry of the wedge tool. For bonds made with typical process parameters, several characteristic values used in the friction power model are determined. The ultrasonic compliance of the bonding system is 2.66μm∕N. A typical maximum value of the relative interfacial amplitude of ultrasonic friction is at least 222nm. The maximum interfacial friction power is at least 11.5mW, which is only about 4.8% of the total electrical power delivered to the ultrasonic generator.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".