Theory and design of quantum light sources from quantum dots embedded in semiconductor-nanowire photonic-crystal systems
Bibliographic record
Abstract
We introduce a platform for realizing on-chip quantum electrodynamics using photonic-crystal waveguide structures composed of periodic nanowire arrays with embedded semiconductor quantum dots to act as quantum light sources. These nanowire-based structures, which can now be fabricated with excellent precision, are found to produce waveguide Purcell factors exceeding 100 and on-chip $\ensuremath{\beta}$ factors up to 99%. We investigate the fundamental optical properties of photonic-crystal waveguides and finite-size structures, using both photonic band structure calculations and rigorous Green function computations which allow us to obtain the modal properties and the local density of photon states. A comparison with slab-based photonic crystals is also made and we highlight a number of key advantages in the nanowire system, including the potential to reduce extrinsic scattering losses and produce high theoretical Purcell factors and $\ensuremath{\beta}$ factors on-chip. We also demonstrate that these structures exhibit rich photonic Lamb shifts over broadband frequencies.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".