Ultrasonic bonding of flexible PCB to rigid PCB using an Sn interlayer
Bibliographic record
Abstract
Purpose The aim of the paper is to study the feasibility of direct ultrasonic bonding between contact pad arrays on flexible printed circuit boards (FPCB) and rigid printed circuit boards (RPCB) at ambient temperature. Design/methodology/approach Metallization layers on the RPCB comprised Sn on Cu while the pads on the FPCB consisted of Au/Ni/Cu. Prepared RPCB and FPCB were bonded by ultrasound at ambient temperature using an ultrasonic frequency of 20 kHz, a power of 1,400 W, and 0.62 MPa of bonding pressure. The bonded samples were cross‐sectioned and the joints and microstructures were observed by Field Emission Scanning Electron Microscopy (FE‐SEM) and Energy Dispersive Spectroscopy (EDS). The soundness of the joints was evaluated by pull testing. Findings Robust bonding between FPCB and RPCB was obtained by bonding for 1.0 and 1.5 s. This result has confirmed that direct room temperature ultrasonic bonding of Au and Sn is feasible. At a longer bonding time of 3.0 s, cracks and voids were found in the joints due to excessive ultrasonic energy. The IMC (intermetallic compound) between the Sn layer and pads of the RPCB was confirmed as Cu6Sn5. On the FPCB side, Cu6Sn5 and Ni3Sn4 were formed by contact with the facing Sn coating, and mechanically alloyed Cu0.81Ni0.19 was found within the pads. Meanwhile, the strength of bonded joints between FPCB and RPCB increased with bonding time up to 1.5 s and the maximum value reached 12.48 N. At 3.0 s bonding time, the strength decreased drastically, and showed 5.75 N. Footprints from the fracture surfaces showed that bonding started from the edges of the metal pads, and extended to the pad centers as ultrasonic bonding time was increased. Originality/value Direct ultrasonic bonding with transverse vibration at ambient temperature between the surface layers of the pads of FPCB and RPCB has been confirmed to be feasible.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".