Work function tuning of reactively sputtered MoxSiyNz metal gate electrodes for advanced CMOS technology
Bibliographic record
Abstract
Due to continued transistor scaling, work function tuning of metal gates has become important for advanced CMOS applications. Specifically, this research has been undertaken to discover the tuning of the MoxSiyNz gate work function through the incorporation of nitrogen. Metal Oxide Semiconductor (MOS) capacitors were fabricated using thermal SiO2 as gate oxide on lightly doped p-type Si wafer. A molybdenum silicide (MoSi2) target was reactively sputtered at 10mTorr in presence of N2 and Ar. The gas flow ratio, RN = N2/ (N2+Ar), was adjusted to vary the nitrogen concentration in MoSiN films. The gate work function (Фm) was extracted from capacitance-voltage (CV) measurements using the VFB-tox method. Interfacial barrier heights were measured using internal photoemission (IPE) as an independent confirmation of the MoSiN gate work function. The work function was found to decrease linearly (from ~4.7eV to ~4.4eV) for increasing gas flow ratios (from 10% to 40%). Secondary ion mass spectrometry (SIMS) depth profiles suggested that the nitrogen concentration was relatively uniform throughout the film. X-Ray Photoelectron Spectroscopy (XPS) surface analysis showed a steady increase in the total nitrogen concentration (from ~20% to 32%) in these films as gas flow ratio was increased. These data suggests that the increase in nitrogen concentration in MoSiN films corresponds directly with the lowering of MoSiN work function. These results clearly demonstrate that the work function of MoxSiyNz can be varied ~0.3 eV by adjusting the nitrogen concentration.
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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) | 0.000 | 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 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".