Oxygen Plasma and Humidity Dependent Surface Analysis of Silicon, Silicon Dioxide and Glass for Direct Wafer Bonding
Bibliographic record
Abstract
Surface and interface characteristics of substrates are critical for reliable wafer bonding. Understanding the elemental and compositional states of surfaces after various processing conditions is necessary when bonding dissimilar materials. Therefore, we investigated the elemental and compositional states of silicon (Si), silicon dioxide (SiO 2 ) and glass surfaces exposed to oxygen reactive ion etching (O 2 RIE) plasma followed by storage in controlled humidity and/or ambient atmospheric conditions to understand the chemical mechanisms in the direct wafer bonding. High-resolution X-ray Photoelectron Spectroscopy (XPS) spectra of O 2 RIE treated Si, SiO 2 and glass showed the presence of Si(-O) 2 resulting in highly reactive surfaces. A considerable shift in the binding energies of Si(-O) 2 , Si(-O) 4 and Si(-OH) x were observed only in Si due to plasma oxidation of the surface. The humidity and ambient storage of plasma activated Si and SiO 2 increased Si(-OH) x due to enhanced sorption of hydroxyls. The amounts of Si(-O) 2 and Si(-OH) x of Si varied in different humidity storage conditions which are attributed to crystal-orientation dependent surface morphology and oxidation. The O 2 RIE plasma induced high surface reactivity and humidity induced Si(-OH) x can play an important role in the hydrophilic wafer bonding with low temperature heating.
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.
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) | 0.001 | 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 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".