All‐Group IV Transferable Membrane Mid‐Infrared Photodetectors
Bibliographic record
Abstract
Abstract Semiconductor membranes emerged as a versatile class of nanomaterials to control lattice strain and engineer complex heterostructures enabling a variety of innovative applications. With this perspective, herein this platform is exploited to tune simultaneously the lattice parameter and bandgap energy in group IV GeSn semiconductor alloys. As Sn content is increased to reach a direct bandgap, these semiconductors become metastable and typically compressively strained. It is shown that the relaxation in released membranes extends the absorption wavelength range deeper in the mid‐infrared. Fully released Ge 0.83 Sn 0.17 membranes are integrated on silicon and used in the fabrication of broadband photodetectors operating at room temperature with a record wavelength cutoff of 4.6 µ m, without compromising the performance at shorter wavelengths down to 2.3 µ m. These membrane devices are characterized by two orders of magnitude reduction in dark current as compared to as‐grown strained epitaxial layers. A variety of experimental tools and optimized calculations are used to discuss the crystalline quality, composition uniformity, lattice strain, and the electronic band structure of the investigated materials and devices. The ability to engineer all‐group IV transferable mid‐infrared photodetectors lays the groundwork to implement scalable and flexible sensing and imaging technologies exploiting these integrative, silicon‐compatible strained‐relaxed GeSn membranes.
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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 | 0.001 | 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".