In Situ Studies of Germanium-Tin and Silicon-Germanium-Tin Thermal Stability
Bibliographic record
Abstract
GeSn and SiGeSn semiconductors provide a wealth of opportunities to enable the realization of mid-infrared photonics in group-IV semiconductors will enable on-chip CMOS optoelectronic systems with a potential impact on chemical and biological sensing, spectroscopy, and free-space communication. Moreover, the fact that these alloys are silicon-compatible will make possible the integration of group-IV-based photonics and optoelectronics with CMOS technology. Efficient group-IV-based light emitting devices and photodetectors can now be implemented using band gap engineering in GeSn and SiGeSn semiconductors, which show indirect-to-direct transition at a particular composition and strain. Besides the potential applications in photonics and optoelectronics, Sn-containing group IV semiconductor alloys and heterostructures are also highly relevant for high-mobility and low-power electronics. Additionally, the control of the composition and structure of SiGeSn alloys and heterostructures are also crucial to implement carbon-free energy conversion devices such thermoelectrics and high-efficiency solar cells. A deep understanding of the structural and morphological stability of GeSn and SiGeSn metastable alloys is of utmost importance in order to achieve the aforementioned technologies. With this perspective, we present in this contribution detailed in situ studies of the evolution throughout thermal processing of both composition and structure of set of monocrystalline binary and ternary Sn-containing group-IV alloys. The investigated layers were grown using an industry compatible metal cold-wall Reduced Pressure AIXTRON TRICENT reactor (RP-CVD) with a showerhead for 200/300mm wafers. The epitaxial layers were grown using Si 2 H 6 , Ge 2 H 6 (10% diluted in H 2 ) and SnCl 4 precursors, and N 2 carrier gas, which warrant reasonable growth rates at growth temperatures in the 350-475 °C range. The growth of GeSn and SiGeSn layers was performed on Si(100) wafers using a low-defect density Ge virtual substrate. Figure 1 displays a representative image of cross-sectional scanning transmission electron microscopy of Si 0.04 Ge 0.84 Sn 0.12 layer. The composition and strain of the grown layers were investigated using a variety of experimental techniques including Raman spectroscopy, Rutherford backscattering spectrometry, x-ray reciprocal space mapping, and energy dispersive x-ray spectroscopy. Subsequently, the as-grown layers were subjected to in situ investigations of their structural and elemental properties as a function of annealing temperatures using low energy electron microscopy (LEEM), photoelectron emission microscopy (PEEM), and nano-Auger spectroscopy. These investigations have unraveled unprecedented insights into the stability of these layers as well as into the dynamics of phase separation in Sn-rich alloys. In the latter, we have traced the formation, evolution, surface diffusion of Sn-rich droplets and clusters. We have also indentified the interplay between Sn concentration and the critical temperature that triggers the alloy instabilities in both binary and ternary layers. The effects of dislocations on the dynamics of phase separation were also identified and elucidated. A theoretical treatment including both thermodynamic and kinetic considerations was developed to discuss the observed phenomena.
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 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 | 0.000 | 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 teacher head, 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".