Characterization of Commercial Epoxy Molding Compounds (EMCs) with High Thermal Conductivity
Bibliographic record
Abstract
As semiconductor devices continue to advance, heat accumulation becomes a major concern, threatening device performance and lifespan. Therefore, efficient heat dissipation becomes a crucial topic in the package development. Recent studies have shown that the incorporation of functional fillers, such as alumina <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{A}1_{2}\mathrm{O}_{3})$</tex>, hexagonal boron nitride (h-BN), and aluminum nitride (AlN) into an epoxy molding compound (EMC) formulation can significantly improve the thermal conductivity. However, these composites are often lab-made with simplified compositions, which may not reflect the complex EMC formulations used in the semiconductor industry. To bridge this gap, we studied the properties of three commercial EMCs with the thermal conductivity of 5 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{W}/\text{mK}$</tex> (denoted as TC-EMC) and benchmark it to a conventional EMC containing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{SiO}_{2}$</tex> fillers. The impact of changing the filler type from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{SiO}_{2}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}1_{2}\mathrm{O}_{3}$</tex> for improved thermal conductivity is discussed, ranging from the specific gravity, thermal stability, thermomechanical properties, moisture absorption and adhesion to copper (Cu) substrates. Understanding of material properties is vital for the development of future materials and to unlock the full potential of advanced semiconductor devices.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".