Optical and Mechanical Properties of Si-Based Thin Films for Photonic Applications
Bibliographic record
Abstract
In this talk, we will review recent advances in the fabrication and characterization of silicon-based thin film structures for photonic applications, including rare earth doped silicon oxynitrides and luminescent silicon carbonitrides. For Si-based materials to be used in solid-state lighting (SSL) and silicon photonics schemes it is necessary to have precise control of the optical emission from these materials. This can be accomplished using rare earth dopants such as Ce, Tb, and Eu to obtain blue, green, and red emissions, respectively. After a brief review of the latest developments in the field, this talk will focus on Eu-doped films, which are attractive for silicon photonic applications due to their intense emission in the visible spectral region [1]. The films are fabricated by electron cyclotron resonance plasma enhanced chemical vapour deposition (ECR-PECVD) and doping is accomplished using a novel hybrid radio frequency (RF) magnetron sputtering source in the ECR-PECVD reactor chamber [2]. This approach contrasts with traditional doping methods which use metal-organic precursors to introduce rare-earth dopant species into the host matrix. We will discuss the relationship between the photoluminescence, elasticity, and hardness of Eu doped silicon oxynitride (SiON) thin films. As the efficiency of photonic devices can be influenced by the mechanical properties of the deposited films, a good understanding of their mechanical properties is required for the integration of these films in photonic devices. SiNx thin films, for example, play an important role in strain engineering of active photonic devices, such as diode lasers fabricated on GaAs or InP substrates. We will discuss results of a collaboration between McMaster and the Université de Rennes in France, aimed at quantifying the effects of structured dielectric thin films on the generation of a mechanical strain field in the semiconductor through tuning the amount of mechanical stress present in the dielectric thin film, as a function of the details of the deposition process [3]. [1] Fahmida Azmi et al., “Tunable Emission from Eu:SiOxNy Thin Films Prepared by Integrated Magnetron Sputtering and Plasma Enhanced Chemical Vapor Deposition”, J. Vac. Sci. Technol. A 40, 043402 (2022); https://doi.org/10.1116/6.0001761 [2] J.W. Miller et al., “Integrated ECR-PECVD and Magnetron Sputtering System for Rare-Earth-Doped Si-Based Materials”, Surface and Coatings Technology 336, 99 (2018); doi: 10.1016/j.surfcoat.2017.08.051 [3] Brahim Ahammou et al., “Strain engineering in III-V photonic components through structuration of SiNx films”, J. Vac. Sci. Technol. B 40, 012202 (2022); doi: 10.1116/6.0001352
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".