Role of Heating on Plasma-Activated Silicon Wafers Bonding
Bibliographic record
Abstract
This paper reports on a comparative study of silicon wafer bonding using reactive ion etching (RIE) vs sequential plasma-activated bonding (SPAB). The study shows the measurement of silicon surface roughness and the investigation of heating influences on the bonding strength and microstructures of silicon∕silicon bonded interfaces as a function of the plasma processing parameters such as plasma time and gas pressure. In SPAB, the surfaces were activated using nitrogen radicals after treatment with RIE plasma for . The surface roughness created via RIE plasma is higher than that of the nitrogen radical. In both methods, although high strength bonding of silicon∕silicon interfaces was achieved before heating, bonding strength was reduced after heating except for the specimens activated for 10 and heated at in the RIE method. This reduction may be attributed to the growing number of voids generated across the bonded interface. High resolution transmission electron microscope observations showed a silicon oxide interfacial layer in the SPAB-processed silicon∕silicon interface, which is thicker than that of the RIE-processed interface without heating. After heating (at for in air), the thicknesses of the interfacial oxide layers were increased for both processes. The increased oxide layer thicknesses after heating are a result of the addition of thermally activated oxygen from water absorbed by the silicon bulk wafers and oxygen intrinsic to bulk silicon.
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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) | 0.000 | 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 | 0.000 |
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".