Plasma Activated Low-temperature Die-level Direct Bonding with Advanced Wafer Dicing Technologies for 3D Heterogeneous Integration
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we have demonstrated a plasma activated low-temperature die-level oxide-oxide direct bonding with advanced wafer dicing technologies. This evaluation used blanket 300-mm silicon wafers. 1 μm Tetraethyl orthosilicate (TEOS) oxide was deposited by plasma-enhanced chemical vapor deposition (PECVD) directly on the silicon (Si) wafer surface, followed by chemical mechanical planarization (CMP). Atomic Force Microscopy (AFM) was used to examine the roughness of the wafer surface before dicing and it showed <; 0.38 nm RMS and <; 0.30 nm R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> . Several dicing technologies such as diamond blade dicing, step-cut blade dicing, bevel blade dicing, and stealth laser dicing were evaluated for this integration scheme. In the end, diamond blade dicing has the most compatibility with many materials, but it led to large chipping on the edges of the die. Stealth laser dicing achieves edge chipping of less than 2 μm, which is the least amount of damage among of all dicing methods tested in this study. In the bonding test, the 10 mm square silicon die was bonded to a 35-mm square silicon substrate. Both silicon die and substrate are of thickness 760 μm. Prior to direct oxide-oxide bonding, both silicon die, and substrate went through a two-step cleaning process. The detailed process of the plasma activated die-level direct bonding is discussed.
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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