Interface broadening due to ion mixing during thin film growth at the radio-frequency-biased electrode in a plasma-enhanced chemical vapor deposition environment
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Bibliographic record
Abstract
The authors show that ion bombardment in the range of tens to a few hundreds of eV, used in ion- and plasma-assisted deposition processes, can lead to thin film growth dominated by subsurface deposition due to subplantation (shallow implantation). This can cause significant interface broadening during the initial stages of film deposition as a result of ion mixing. First, by studying the modifications of a c-Si(100) target exposed to an O2 plasma at the radio-frequency (rf)-biased electrode using in situ real-time spectroscopic ellipsometry (RTSE), the authors detect implantation, damage, and oxidation to a depth of up to ∼10nm. They validate these results using high resolution transmission electron microscopy and simulate the effects of ion-surface interactions at the rf-biased electrode by using Monte Carlo TRIDYN simulations. The simulation code, which was modified specifically to consider a broadband ion energy source, enabled the authors to reproduce depth and time relevant experimental results with good agreement. In situ RTSE was then used to monitor TiO2 deposition on SiO2 under similar ion bombardment conditions. The authors observed the formation of a 2-to4-nm-thick interfacial layer, depending on the ion-to-neutral flux ratio (ϕi∕ϕn), which was controlled by varying the deposition rate. TRIDYN simulations revealed that oxygen subplantation causes interfacial broadening during the growth through ballistic mixing of Ti and Si atoms at the interface; the interface width scales as ∼(ϕi∕ϕn)1∕2. Intensive ion mixing at ϕi∕ϕn>1 is also shown to be responsible for the ballistic displacement of the majority of surface-deposited Ti atoms into the bulk, so that the growth appears to be dominated by subsurface deposition under conditions of intense ion bombardment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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