(Invited) Transport and Electroluminescence Properties of Size-Controlled Silicon Nanocrystals Embedded in SiO<sub>2</sub> Matrix Following the Superlattice Approach
Bibliographic record
Abstract
In this invited talk, we will report our latest advances in the field of nanostructured silicon-based light emitters with special emphasis in those that are rare earth doped. We have recently shown that it is possible to push external quantum efficiencies over the 10% by fine tuning the material composition and by using a new device concept that we have called hot electron engineering. Most of the rare earth amount introduced in the host material must be optically active. This can be accomplished by using a deposition method that introduces the rare earth during film growth and by a suitable passivating annealing procedure that: i) does not promote rare earth precipitation and clustering; ii) brings the rare earth to the 3+ oxidation state and iii) reduces non radiative defects. Additionally, for electroluminescent devices the matrix composition must be engineered so that it allows conduction-band transport in contrast with defective systems in which transport is hopping-like. The hot electron engineering concept relies on the fact that the rare earth excitation process in Si-based dielectric matrices proceeds mainly via impact excitation by hot electrons. Thus, devising suitable structures for which electrons can be accelerated to average energies that are resonant to the rare earth excitation energies, will improve both efficiency and maximum power emission. Additionally, driving the device with pulsed polarization and short duty cycle will improve device efficiency and lifetime. Finally, the structures must allow at the same time significant light extraction efficiency. This can be accomplished by improving the antireflective properties of the whole stack and/or by nanostructuring the ITO or polysilicon electrode. We will be show silicon oxides, nitrides and oxynitrides as hosts for the rare-earth species and, additionally, we will display the advantages and drawbacks of introducing small percentages of carbon and aluminum. The rare-earth species incorporated in those matrices are Er, Ce, Tb, Eu and Nd and their light emission efficiency depend in a complex way on matrix composition, material processing, device structure and device polarization. The simplest hot electron engineering structures are bilayers and trilayers with silicon oxide as accelerator. More complex structures like multilayers and superlattices will be also introduced and optimal designs will be proposed. Some reliability issues and potential solutions for them will be also presented. We acknowledge existing collaborations in this field with teams of the McMaster University at Hamilton (Canada), Institut de Microelectrònica de Barcelona (Spain), Nanophotonics Technology Center of Valencia (Spain), Instituto de Óptica of Madrid (Spain), Ion Beam Institute at Rossendorf (Germany), IMTEK at the University of Freiburg (Germany) and CIMAP-CNRS at Caen (France).
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".