Reliability and Performance of Wafer Level Fan Out Package for Automotive Radar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Embedded wafer level ball grid array (eWLB) or Fan-out wafer-level packaging (FO-WLP) is investigated as a package for Monolithic Microwave Integrated Circuit (MMICs) for automotive radar applications in the 77GHz range. Special focus is put on the thermo-mechanical performance to achieve automotive quality targets. The typical fatigue modes “solder ball fatigue” and “copper fatigue”, evolving during thermo-mechanical stress like cycling on board will be discussed. Simulation as well as experimental preparation results for typical fatigue levels are given. In addition, several influencing parameters are listed and rated regarding their effectiveness. The theoretical framework why solder ball fatigue is the only failure mode causing electrical failure is provided. The impact of different thermo-mechanically driven fatigue modes is discussed. Two important parameters to be considered for the functionality of the Radar system are Radio Frequency (RF) and thermal performance. For elaborating the RF performance with present fatigue modes, the phase shift between different channels and pads is analyzed by full-wave electromagnetic (EM) simulation. It is found that for fatigue levels up to 90% the phase shift stays below specification for single fatigue modes and may approach specification only for an unlikely combination of all 90% fatigue modes. For assessing the thermal performance with present fatigue modes, thermal simulation as well as thermal measurements are used. Assuming 50% degradation in average for all thermal balls, an increase of the thermal resistance (Rth) of up to about 30% is seen. On average for all thermal measurements, the deviation between measurement and simulation is within ΔT = ±1°C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it