THz quantum cascade lasers for operation above cryogenic temperatures
Bibliographic record
Abstract
High temperature operation of terahertz (THz) sources based on quantum cascade lasers (QCLs) is discussed. THz QCLs are compact, powerful sources but can only operate at cryogenic temperatures. State-of-the art THz QCLs are made with GaAs/AlGaAs heterostructures and use a single composition of AlGaAs for the barrier material. It was recently shown that multi-composition barriers in the band structure can result in gain > loss at temperature as high as ~240K. We demonstrate early experimental results that yield QCLs that operate up to 184K – similar to QCLs based on single composition barrier designs. An alternative method of producing room-temperature THz is based on intra-cavity difference-frequency generation (DFG) in mid-infrared (mid-IR) QCLs. Here we report devices with record conversion efficiency. THz DFG QCLs reported previously are highly inefficient since THz radiation produced more than ~100 μm away from the exit facet is fully absorbed due to high THz losses in the QCL waveguide. Our lasers use a non-collinear Cherenkov DFG scheme to extract THz radiation from the active region. Dual-color mid-IR quantum cascade lasers with integrated giant optical nonlinearity are grown on semi-insulating (S.I.) InP substrates. THz radiation is emitted at an angle into the substrate with respect to the mid-infrared pumps. Since S.I. InP is virtually lossless to THz radiation, this scheme allows for efficient extraction of THz radiation along the whole waveguide length. As a result, our sources demonstrate large mid-infrared-to-THz conversion efficiency. Devices tested at room-temperature produced 18μW peakpower and 75μW/W2 conversion efficiency.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".