Bibliographic record
Abstract
Nonlinear optics, a research area that is emerged after the invention of laser in 1960, continues to be widely explored with a broad range of applications from optical communication and spectroscopy to quantum photonics. A long-standing goal is to realize nonlinear optical structures at progressively low optical power down to quantum regime and electrically-tunable, which is difficult given the small nonlinear coefficients of bulk materials. Currently, there arises a new type of 2D nonlinear optical materials with fascinating properties such as broadband saturable absorption and ultrafast carrier dynamics with a large nonlinear refractive index. Graphene with the ease of fabrication, compatibility with CMOS technology and silicon photonics is a strong contender for a new class of optoelectronic and photonic devices and circuits. In this talk, I will first review graphene’s relevant physics for its application in nonlinear optics complemented by our theoretical work on the quantum treatment of its nonlinear Kerr coefficient. This includes how the Kerr coefficient can be electrically-tuned for device operation as well as new physics of anomalous optical saturation. I will then present our systematic experimental investigation of measuring Kerr coefficient through optical self modulation effect, with an emphasis on its wavelength dependence and temporal evolution via combined z-scan and pump-probe measurements. Finally, I will present our experimental work on ultrafast optical modulation/switching and bistability in a hybrid graphene-silicon photonic crystal nanocavities providing a world-record of modulation/switching speed and depth with the lowest optical power for an integrated nonlinear silicon-based photonic devices.
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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".