Tailoring of low-temperature-grown gallium arsenide for all-optical device applications
Bibliographic record
Abstract
Future communications and computing systems will require advanced capabilities to handle the increasing requirements for ever-faster and higher-bandwidth operation. To move beyond the system limits imposed by electronic components, researchers are investigating the use of all-optical components for ultrafast operations such as switching, and time-division multiplexing and demultiplexing. The requirement for operating times to be significantly faster than those possible with electronics means that the optical components must operate in the picosecond or sub-picosecond range. We can also project further desirable features of this new ultrafast technology. All-optical devices require a material with an optical nonlinearity so that one optical beam can control another. Compact devices will require large nonlinearities, so that switching can be effected at modest power levels. Another desirable feature relates to the integrability of these ultrafast switching components with other optoelectronic components such as lasers and detectors. This clearly favors a III-V semiconductor based device. Although normal III-V semiconductor materials exhibit large optical nonlinearities near the band edge, they have a relatively slow (nanosecond) response due to the slow recombination times for the optically-excited carriers. This paper reports our new findings on how the nonlinear optical properties of low temperature-grown GaAs can be tailored via growth and subsequent annealing conditions to yield a material with the desired characteristics for a specific ultrafast device.
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".