Electro-optical properties of silicon nanocrystals
Bibliographic record
Abstract
In the last decade, the luminescent properties of silicon nanocrystals (Si-nc) have been increasingly studied, since Si-nc are considered as good candidates for optical interconnects between ever-smaller integrated circuits (ICs) components, and for the monolithic integration of all-silicon photonic and electronic devices. For these applications, an efficient coupling between optical and electrical signals within Si-nc structures is required. In this article, the interaction between simultaneous optical and electrical stimulation of Si-nc is examined. To this end, the photoluminescence (PL) spectra of Si-nc obtained by ion implantation in a thin (40 to 60 nm) oxide layer of metal-oxide-semiconductor (MOS) devices has been recorded as a function of variable applied voltage biases at room temperature. Two remarkable features have been observed: an optical memory effect, due to asymmetric PL intensity modulation with respect to biasing polarity, and an efficient optical switching of an electric current in reverse bias operation. These results are explained in terms of the competing effects of the storage and the photogeneration of charge carriers in Si-nc and oxide defects, as indicated by the correlation between the PL intensity and the current flowing through the MOS devices. Moreover, the use of positively- and negatively- doped substrates in the MOS structures distinctly shows the different effects of electron injection over hole injection in Si-nc and their surrounding SiO2 matrix. These novel optoelectrical features of Si-nc are expected to add more functionality to future all-silicon photonic and electronic ICs.
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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".