Thin film reaction of transition metals with germanium
Bibliographic record
Abstract
A systematic study of the thermally induced reaction of 20 transition metals (Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W, Mn, Re, Fe, Ru, Co, Rh, Ir, Ni, Pd, Pt, and Cu) with Ge substrates was carried out in order to identify appropriate contact materials in Ge-based microelectronic circuits. Thin metal films, nominally 30nm thick, were sputter deposited on both amorphous Ge and crystalline Ge(001). Metal-Ge reactions were monitored in situ during ramp anneals at 3°Cs−1 in an atmosphere of purified He using time-resolved x-ray diffraction, diffuse light scattering, and resistance measurements. These analyses allowed the determination of the phase formation sequence for each metal-Ge system and the identification of the most promising candidates—in terms of sheet resistance and surface roughness—for their use as first level interconnections in microelectronic circuits. A first group of metals (Ti, Zr, Hf, V, Nb, and Ta) reacted with Ge only at temperatures well above 450°C and was prone to oxidation. Another set (Cr, Mo, Mn, Re, Rh, Ru, and Ir) did not form low resistivity phases (<130μΩcm) whereas no reaction was observed in the case of W even after annealing at up to 1000°C. We found that Fe, Co, Ni, Pd, Pt, and Cu were the most interesting candidates for microelectronic applications as they reacted at relatively low temperatures (150–360°C) to form low resistivity phases (22–129μΩcm). Among those, two monogermanides, NiGe and PdGe, exhibited the lowest resistivity values (22–30μΩcm) and were stable over the widest temperature window during ramp anneals. In passing, we note that Cu, Ni, and Pd were the most effective in lowering the crystallization temperature of amorphous Ge, by up to 290°C for our typical ramp anneals at 3°Cs−1.
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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) | 0.001 | 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".