Temporary Bonding Technologies for Thin Wafer Handling
Bibliographic record
Abstract
Thin glass substrates offer low dielectric constant and high temperature process capability for faster and thinner packages but handling is challenging due to lack of mechanical stiffness. Temporary bonding of thin wafers to stiffer carrier substrates offers a solution and has been an active area of research and development in recent years. Polymeric and tape wafer bonding solutions are available but usually for low temperature processes. In Corning, we have developed an array of surface treatment/coating technologies that offer high temperature capability (as high as 700°C). The common theme of all these technologies entails surface treatment or coating of a carrier substrate to achieve strong non-covalent bonding with the thin wafer. The desired attributes are: (a) high enough bond energy at room temperature to withstand vacuum, thermal, and wet processing steps, (b) low enough bond energy after the thermal processing steps rendering the pair mechanically separable, (c) spontaneous bonding via self-propagating bond wave, and (d) minimal outgassing or bubble formation between carrier and thin wafer due to degradation of bonding material. The technologies range from vacuum based sub-monolayer surface functionalization to vacuum based organometallic coating to high throughput solution processed ultra-thin molecular coatings. In this talk we will first describe the theoretical underpinnings of various surface and chemical forces that control the bond energy between the carrier and the thin wafer, models to predict the bond energy as function of surface chemistry and surface energy parameters, influence of surface chemistry on various process attributes such as bond wave propagation and outgassing during thermal treatment. We will also briefly review various processes developed and their relative strengths and applicability.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".