Wire bonded 3D coils render air core microtransformers competitive
Bibliographic record
Abstract
We present a novel wafer-level fabrication method for 3D solenoidal microtransformers using an automatic wire bonder for chip-scale, very high frequency regime applications. Using standard microelectromechanical systems fabrication processes for the manufacturing of supporting structures, together with ultra-fast wire bonding for the fabrication of solenoids, enables the flexible and repeatable fabrication, at high throughput, of high performance air core microtransformers. The primary and secondary solenoids are wound one on top of the other in the lateral direction, using a 25 µm thick insulated wire. Besides commonly available gold wire, we also introduce insulated copper wire to our coil winding process. The influence of copper on the transformer properties is explored and compared to gold. A simulation model based on the solenoids' wire bonding trajectories has been defined using the FastHenry software to accurately predict and optimize the transformer's inductive properties. The transformer chips are encapsulated in polydimethylsiloxane in order to protect the coils from environmental influences and mechanical damage. Meanwhile, the effect of the increase in the internal capacitance of the chips as a result of the encapsulation is analyzed. A fabricated transformer with 20 windings in both the primary and the secondary coils, and a footprint of 1 mm 2 , yields an inductance of 490 nH, a maximum efficiency of 68%, and a coupling factor of 94%. The repeatability of the coil winding process was investigated by comparing the data of 25 identically processed devices. Finally, the microtransformers are benchmarked to underline the potential of the technology in rendering air core transformers competitive.
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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 | 0.002 |
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".