Optical wavelength converters for photonic band gap microcircuits
Bibliographic record
Abstract
We demonstrate compact optical wavelength conversion architecture for picosecond laser pulses and data streams within photonic band gap waveguides. These multimode waveguides are seeded with resonantly driven, inhomogeneously broadened, distributions of quantum dots whose optical transition center frequency is placed near a sharp discontinuity in the local (electromagnetic) density of states (LDOS). This discontinuity is provided by a cutoff in one of the waveguide modes. Wavelength conversion of an optical pulse propagating in the single-mode spectral range of the waveguide, near the LDOS jump, is provided by a steady-state holding field with frequency matched to the center frequency of the quantum dot distribution. In the absence of an incident laser pulse, the holding field is absorbed by the quantum dots. When the incident pulse intensity is sufficient to cause population inversion of a suitable fraction of the quantum dots, the holding field is amplified and the incident pulse profile is imprinted on to the holding field, leading to wavelength conversion in the range of 3--20 nm at wavelengths near $1.5\text{ }\ensuremath{\mu}\text{m}$. Larger wavelength shifts typically require higher power levels for operation (milliwatt scale) but enable conversion of shorter (picosecond) pulses. Small wavelength shifts typically require narrower distribution of quantum dot resonance frequencies. Using finite-difference time-domain simulations, we show that an optical pulse (Gaussian in time) can create another equivalent optical pulse with either higher or lower center frequency inside the photonic band gap of the structure. Optical pulses of a given center frequency can also be selectively amplified or absorbed depending on the coincident arrival of another laser pulse with different center frequency, enabling all-optical logic operations within a multiwavelength-channel optical circuit.
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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".