(Invited) New Strategies to Produce Light Emitters with Si-based Nanostructures
Bibliographic record
Abstract
Silicon-Germanium strain engineering has been used for more than two decades in silicon based devices and has contributed to the scaling down of a transistor’s size and to significant improvements in device performance. However, while conventional silicon-germanium based electronics has experienced rapid and steady growth, thanks to this continuous miniaturization of transistors, this trend cannot continue indefinitely. Industry has already moved to alternate methods such as FinFET devices, in which a thin silicon channel is placed vertically, and the FD-SOI (FD-SGOI) design consisting of a thin film Si(SiGe) channel placed horizontally. For nodes scaled down below 28 nm, low power operation will be inherently hindered by both the imperfect interface, non-uniformity of ultra-thin films and quantum confinement effects, which increase the effective bandgap. In these devices, despite the intense research activity on the strained SiGe ultra-thin body, which accounts for a large portion of such microelectronic devices (below the 45 nm node), we still fail to properly understand the mechanisms that limit hole and electron mobilities in SGOI layers. In addition, one of the main challenges for Si based devices remains the fabrication of efficient group-IV photon sources / photon detectors compatible with the microelectronic industry, which would usefully replace the integration of III-V heterostructures on Si. The major bottleneck is that group-IV semiconductor elements have indirect bandgaps, but with possibilities of being transformed to direct bandgaps using strain engineering strategies. In this presentation, we will review the formation mechanism of Ge-rich layers on SOI by condensation at different temperatures. TEM cross-section and GPA analysis of the heterostructures will be presented. We will also report the physical and optical properties of these heterostructures. Special attention is devoted to the influence of the SiGe thickness reduction (up to few MLs), where quantum confinement is prominent in the optical properties of the layers. Raman and PL results will be presented to better explain such confinement behavior. We show that novel SGOI substrates could represent a key strategy for the fabrication of future photonic devices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".