Heterogeneous integration of transfer bonded terahertz quantum cascade lasers for improved heat management and light coupling
Bibliographic record
Abstract
Terahertz quantum cascade lasers (THz QCLs) in metal-metal (MM) ridge waveguides have been fabricated and hetero bonded on aluminum nitride (AlN) substrate as heat sink submount. Compared with the conventional structure of THz QCLs in MM waveguides on gallium arsenide (GaAs) as receptor substrate, AlN performs superior heat dissipation properties for thermal management due to its much higher thermal conductivity. The light–current density–voltage (L-J-V) characterization shows comparable maximum operating temperature (Tmax) at 93-95 K for both THz QCLs bonded on AlN and GaAs under short pulse injection (250 ns). However, as the injected pulse duration increases for THz QCLs on GaAs, the light intensity drops quickly, eventually leading to lasing quenching when the pulse duration is above 30 µs at 80 K (heat sink temperature). On the other hand, THz QCL on AlN shows much stronger light intensity and slower decrease with the increase of the pulse duration; for example, the light intensity is 100 times higher for the THz QCL on AlN (pulse duration of 40 µs) than THz QCL on GaAs (pulse duration of 30 µs) at the same measurement conditions at 80 K. This study shows suspected joule heating plays a great role on THz QCLs operating from long duty cycle towards continuous-wave (CW) mode, indicating AlN substrate as a high thermal conductivity material produces superior thermal management for heat extraction and dissipation.
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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".