Complex oxide thin films towards surface-phonon-polariton-based infrared optoelectronics [Invited]
Bibliographic record
Abstract
Infrared nanophotonics offers enormous potential for enhancing infrared optoelectronic technologies, due to the ability to confine light to deeply sub-wavelength dimensions. One of the limitations of traditional nanophotonics approaches is the inherent losses present in the plasmonic materials that are conventionally used. Surface phonon polaritons offer a lower-loss alternative but are more difficult to integrate with conventional III-V-based semiconductors used for infrared optoelectronics. In this work, we examine the properties of complex oxides, which can be grown directly onto III-V semiconductors for the purpose of infrared light detection. We grow films of both SrTiO 3 and BaTiO 3 on GaAs using pulsed laser deposition, and examine their properties using a combination of X-ray diffraction, atomic force microscopy, transmission electron microscopy, and infrared reflectance spectroscopy. We find that the films grown exhibit good crystallinity with smooth, uniform surfaces, and with occasional minor misoriented grains. Their optical properties indicate higher losses than perfect single crystal substrates, but by less than a factor of two, with phonon Q factors of 18-60, which is favorable when compared with plasmonic materials. We then conduct numerical simulations, which show that these films can be used to create surface phonon polariton infrared detectors that outperform similar metallic gratings by a factor of four. Our results show that the integration of oxide materials with conventional semiconductors is a viable route to improving infrared detector technology, competitive with other nanophotonics approaches.
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 | 0.000 | 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".