Characterization and Performance Improvement of Bondwire Interconnects in QFN Packages for Bandpass mm-Wave Applications
Bibliographic record
Abstract
In this article, the input and output impedances of the bondwire interconnects in quad flat no-lead (QFN) packages are extracted and analyzed over the wide frequency range of dc–100 GHz. It is shown that knowing the impedance parameters of a transition makes it simpler and faster to optimize the structure for a desired operating frequency. Using extracted impedance properties, a compensation bondwire interconnections method based on only one quarter-wavelength impedance transformer line on the printed circuit board (PCB) side is proposed. It is shown that this technique can easily improve the bandpass impedance matching of the bondwire at any desired frequency up to 50 GHz. The bondwire transition is found to be interchangeably capacitive and inductive for frequencies above 50 GHz. Therefore, another simple and effective method based on the defected ground structure (DGS) concept are proposed to improve the bandpass performance of the QFN packaging for frequencies up to 80 GHz. At each step of this work, commercial three-dimensional (3-D) electromagnetic (EM) field software is used to analyze and verify the microwave characteristics of the proposed methods. Both matching network topologies are implemented on the PCB side, without any on-die compensation circuit, which reduces the cost, time, and complexity of the design process and implementation.
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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 | 0.000 |
| 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) | 0.000 | 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".