Temporary Bonding for High Temperature Processing of Thin Glass Using Plasma Activated DLC Layer
Bibliographic record
Abstract
Commercial temporary bonding technologies utilize relatively thick polymeric materials whose use is limited to below 400oC, typically below 250oC. While adequate for many BEOL processes, the temperature limitation and outgassing from the bonding media are incompatible with higher temperature processes such as CVD growth, Au eutectic bonding and other processes for MEMS, photonics, or packaging applications. This talk describes a temporary van der Waals wafer bonding method using a thin continuous layer of PECVD deposited diamond like carbon (DLC) layer suitable for smooth glass, silicon and sapphire substrates. Rapid self-propagating bonding is achieved through plasma activation of the bonding surface. Raman and optical characterization of the DLC layer are consistent with a hydrogenated amorphous carbon structure. A 4 nm thick N2-O2 treated DLC layer is shown to bond thin glass to a display glass carrier with a bond energy <500mJ/m2 and minimal blistering at temperatures up to 600C. Bond energy of thin glass bonded with DLC was shown to be less than 400 mJ/m2 throughout a simulated LTPS TFT thermal cycle. The DLC bonding layer remains adherent throughout the vacuum, thermal and wet processing steps of typical semiconductor and MEMS fabrication; yet the bond energy between the pair remains low-enough after the thermal processing steps that renders the pair fully detachable.
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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) | 0.003 | 0.001 |
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".