Enhancing minority carrier lifetime in Ge: Insights from HF and HCl cleaning procedures
Bibliographic record
Abstract
Efficiently passivating germanium (Ge) surfaces is crucial to reduce the unwanted recombination current in high-performance devices. Chemical surface cleaning is critical to remove surface contaminants and Ge oxides, ensuring effective surface passivation after dielectric deposition. However, Ge oxides can rapidly regrow upon air exposure. To understand the surface evolution after wet cleaning, we present a comprehensive study comparing HF and HCl deoxidation steps on p-type Ge surfaces and monitor the surface as a function of air exposure time. Distinct oxide regrowth dynamics are observed: HF-treated samples exhibit swift regrowth of all Ge oxide states, whereas HCl-treated Ge surfaces exhibit a lower concentration of low degrees of oxidation and slower or no regrowth of high oxide states even after 110 min of air exposure. In addition, the presence of Ge–Cl bonds induces different oxidation dynamics compared to the Ge–OH bonds resulting from HF cleaning. This leads to varying surface electronic band structures, with HF-treated Ge exhibiting a strong positive band bending (+0.20 eV). Conversely, HCl-treated samples display a lower band curvature (+0.07 eV), mostly due to the presence of Ge–Cl bonds on the Ge surface. During air exposure, the increased GeOx coverage significantly reduces the band bending after HF, while a constant band bending is observed after HCl. Finally, these factors induce a reduction in the surface recombination velocity after wet etching. Combining both chemical and field-induced passivation, HF-treated Ge without rinsing exceeds 800 μs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".