Mid‐Infrared Top‐Gated Ge<sub>0.82</sub>Sn<sub>0.18</sub> Nanowire Phototransistors
Bibliographic record
Abstract
Abstract Achieving high crystalline quality germanium‐tin (Ge1 − xSnx) semiconductors at Sn content exceeding 10% is quintessential to implement the long sought‐after silicon‐compatible mid‐infrared photonics. Herein, by using sub‐20 nm Ge nanowires as compliant growth substrates, Ge1 − xSnx alloys with a Sn content of 18% exhibiting a high composition uniformity and crystallinity along a few micrometers in the nanowire axial direction are demonstrated. The measured bandgap energy of the Ge/Ge0.82Sn0.18 core/shell nanowires is 0.322 eV enabling the mid‐infrared photodetection with a cutoff wavelength of 3.9 µm. These narrow bandgap nanowires are also integrated into top‐gated field‐effect transistors and phototransistors. Depending on the gate design, these transistors are found to exhibit either ambipolar or unipolar behavior with a subthreshold swing as low as 228 mV/decade at 85 K. Moreover, varying the top gate voltage from ‐1 to 5 V yields nearly one order of magnitude increase in the photocurrent of the nanowire phototransistor under a 2330 nm illumination. This study shows that the core/shell nanowire architecture with a very thin core not only mitigates the challenges associated with strain build‐up observed in thin films but also provides a promising platform for all‐group IV mid‐infrared photonics and nanoelectronics paving the way toward sensing and imaging applications.
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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".