Germanium Tin Surface Passivation and Its Effect on the Optoelectronic Performances
Bibliographic record
Abstract
Germanium Tin is an emerging semiconductor, with high carrier mobility and tunable bandgap directness and energy, that has been attracting a great deal of interest for applications in silicon-compatible electronics and monolithic optoelectronics. In contrast to compound semiconductors which have in the limelight to address several challenges in these technologies, this new emerging family of silicon-compatible group IV semiconductors holds the promise to combine both cost effectiveness and performance in several devices such as tunnelling field effect transistors, infrared detectors and emitters. By tuning strain and composition of GeSn, the band structure can be modified, thus allowing to engineer a large variety of low-dimensional heterostructures relevant to these devices. In fact, even though Ge is an indirect bandgap material, the incorporation of Sn in its lattice allows a transition into a direct bandgap material. Thus, GeSn yields to higher rate of radiative transitions and band-to-band tunneling. However, there are still several outstanding challenges at the materials level that must be overcome before harnessing GeSn advantageous properties. For instance, effective processes to effectively passivate its surface at various Sn composition are yet to be established. It is known that the surface of a semiconductor contains electronically active states because of unsaturated surface bonds or dangling bonds states, which act as localized energy levels the band gap that can change the intrinsic electrical behavior of the material. Therefore, understanding and controlling the surface states are of compelling importance. Moreover, the native oxide layer that forms at the surface of GeSn contains defects resulting in trapping charge carriers thus decreasing their mobility. The poor quality of the surface can decrease the rate of radiative transitions and contribute to the dark current. An effective passivation is thus needed to enhance the optoelectronic performances of GeSn. Passivation layer allows improved charge-separation, reduces charge recombination at surface states, passivates the dangling bonds and decreases the reactivity of the surface. To address these issues, this work investigates several chemical passivation processes and evaluate their effects on the optoelectronic properties of GeSn. The GeSn samples investigated in this work were grown on silicon wafers using ~0.6-3 µm-thick Ge interlay – commonly known as virtual substrates (Ge-VS). The epitaxial growth was carried out by the chemical vapor deposition (CVD) using monogermane (GeH 4 ) and tin-tetrachloride (SnCl 4 ) precursors. Strain minimization and the reduced growth temperature below 350 °C are of paramount importance to enhance Sn incorporation in Ge lattice to reach compositions of 7 – 17 at.%, much larger than the equilibrium composition of 1at.%. Several passivation treatments were evaluated and their basic mechanisms were elucidated. We first evaluated the capacity to remove the native oxide, leave the surface clean and passivate the dangling bonds of GeSn. The kinetic of oxide regrowth was studied to assess the chemical stability of the passivation through X-rays photoelectron spectroscopy (XPS) analysis. For instance, for a surface treatment consisting of a dip in HF followed by a dip in (NH 4 ) 2 S and then by a nitride drying. XPS measurements show that it is effective in cleaning and passivating the surface, it allows removing the major part of Ge and Sn oxides. Recorded spectra reveal that this treatment gives rise to a persistent sulfur bonds at 161.8 eV, which is the signature of sulphide compounds. Sulfur allows surface passivation and slows down the oxide regrowth. Moreover, alternative surface passivation processes will also be discussed, and their performances compared to the treatment above will be elucidated. Besides, to better understand the evolution of the surface state after the treatments, ellipsometric measurements combined with atomic force microscopy studies are conducted and will be presented. Also, complementary electrical measurements and photoluminescence emission studies will also be discussed to highlight the effectiveness of each treatment in improving the optoelectronic performances of GeSn.
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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) | 0.000 | 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".