Bibliographic record
Abstract
This thesis is a sandwich thesis composed of three papers that are published in refereed journals or conferences. The first paper is a systematic experimental study conducted to identify the first phase to form during cobalt germanidation. Hexagonal β-Co5Ge3 was the first phase to form at temperatures as low as 227°C followed by monoclinic CoGe as the second phase at the same temperature. We also report for the first time that both phases that formed were highly ordered partial epitaxial crystal orientations suggesting that both of those low-temperature phases could potentially serve as high quality contacts for germanium based devices with a very low thermal budget which is advantageous for the process design. Those results contributed to a better understanding of cobalt germanidation leading to the first multiphase technology computer aided design model presented in the second paper. This kinetic model for cobalt germanide growth can predict the resulting phase based on anneal time, temperature, and ambient. The model has been calibrated to experimental results. This predictive model can help in the design of cobalt germanide contacts with low resistance and can serve as a general modeling framework for multiphase solid state reaction binary systems. A comprehensive survey of the experimental results for formation of cobalt germanides is discussed and the data are reconciled in the third paper. Factors affecting the resulting phases and their quality are identified and some optimum choices for the experimental parameters are pointed based on the survey. The role of germanium crystal orientation in ohmic and Schottky properties of the contact is analyzed. Fermi level pinning plays a role mainly on metal/(100) n-type Ge interfaces and its role is minimal on p-type Ge and other crystalline orientations. Schottky Barrier Heights for cobalt germanide contacts reported in the literature are surveyed. Crystalline cobalt germanides, forming when Co is deposited at high temperatures, are expected to have lower interface resistivities compared to those reported. The work is important because contact resistance has become one of the most important factors in advanced complementary metal oxide semiconductor (CMOS) technology and advanced devices already include germanium (Ge) in the source/drain regions of devices. It is also important because heating at the interface due to contact resistance is one of the key challenges in power devices and cobalt germanide can be used both for Si and Ge based devices as well as for gallium nitride (GaN) devices. The latter application is possible because cobalt germanide is lattice-matched to GaN.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.418 | 0.001 |
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; both teacher heads agree on what is shown here.
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".