Nanoscale Wire Bonding of Individual Ag Nanowires on Au Substrate at Room Temperature
Bibliographic record
Abstract
Gold (Au) wire has been used for decades in wire bonding, a technique to interconnect an integrated circuit chip with metal leads in the semiconductor industry [ 1 ]. The cost of Au wire has significantly increased in recent years [ 2 ]. This has prompted the study and use of alternatives such as silver (Ag) [ 3 , 4 ], copper (Cu) [ 5 – 7 ], and Ag/Au alloys [ 8 , 9 ]. Cu wire suffers from oxidation issues, as well as a high hardness and Young’s modulus. Thus, it is difficult to bond. Various intermetallic compounds have been prepared that would affect the efficiency of a device and thus reduce its lifetime [ 10 ]. Currently, cost concerns are leading to wire diameter decreases, which is made possible to increase the packing density using finer pitches. Controllable bonding or welding at a submicrometer scale or nanoscale is still a great challenge [ 11 ]. Many efforts have been made to push the size limitation down to the nanoscale [ 12 ], including nanoscale resistance spot welding [ 13 , 14 ], nanoscale soldering [ 15 , 16 ], and ultrasonic bonding [ 17 ]. Because of the small energy requirement [ 11 ] and reactivity of nanomaterials, some new bonding methods have been reported based on novel concepts, including the cold welding of Au and Ag nanowires (NWs) by oriented attachment [ 18 , 19 ], plasmonic welding of Ag NWs with plasmonic effects [ 20 , 21 ], nanowelding using a scanning probe microscope [ 22 ], and optically controlled nanosoldering [ 23 ].
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".