Material and Integration Issues for Rare Earth Silicides as Gate and Diffusion Contacts in Advanced CMOS Technologies
Bibliographic record
Abstract
In an integration scheme where the nFETs and pFETs of CMOS devices are silicided with different materials (Dual Silicides), rare-earth erbium (Er) and ytterbium (Yb) silicides are potential candidates for contacts to n-Si because of their lower Schottky barrier heights, as compared to more conventional nickel and cobalt silicides. [1-3] Although the lower Schottky barrier across the silicide/n-silicon interface results in reduced contact resistivity, the microstructure can exhibit defects and morphology issues [3-7] which affect device integrity and may contribute to contact resistance degradation. [8] In this study, we compared the material and integration properties of Er and Yb silicides with those of Ni (Pt-alloyed) silicide. Using four point probe, AFM, optical inspection and SEM, we compared the silicides using sheet resistance, surface morphology, defects density and ease of formation in narrow lines. We found that the silicide morphology is affected by several process parameters such as the type of metal deposition process (sputtering vs. evaporated) and the anneal formation temperature. The silicides were also tested for their ability to withstand aggressive processing after their formation. The processes tested included exposure to PECVD plasma, contact hole reactive ion etching and forming-gas annealing. The rare-earth and Ni(Pt) silicides showed similar stability upon processing. Lastly, we quantified the residual metal remaining on dielectric surfaces after silicide processing. Overall, Er silicide showed better performance than Yb silicide. By optimizing various elements of the silicidation process, higher quality silicide films can be achieved for evaluation as suitable nFet contacts.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".