Three-band quantum well infrared photodetector using interband and intersubband transitions
Bibliographic record
Abstract
This paper presents the design, fabrication, and characterization of a quantum well infrared photodetector capable of detecting near infrared (NIR), midwavelength infrared (MWIR), and long wavelength infrared (LWIR) simultaneously. The NIR detection was achieved using interband transition while MWIR and LWIR were based on intersubband transition in the conduction band. The quantum well structure was modeled by solving self-consistently the Schrödinger and Poisson equations with the help of the shooting method. Intersubband absorption in the sample was measured for the MWIR and LWIR using Fourier transform infrared spectroscopy, and the measured peak positions were found at 5.3 and 8.7 μm, respectively, which are within 5% of the theoretical values, indicating the good accuracy of the self-consistent model. The photodetectors were fabricated using a standard photolithography process with exposed middle contacts to allow separate bias and readout of signals from the three wavelength bands. The background limited infrared performance for the LWIR quantum wells shows an upper operating temperature of about 70 K, limiting the overall device. Photocurrent spectroscopy was performed and gave three peaks at 0.84, 5.0, and 8.5 μm wavelengths with approximately 0.5, 0.03, and 0.13 A/W peak responsivities for NIR, MWIR, and LWIR bands, respectively. These results demonstrate the possibility of detection of widely separated wavelength bands, in a single pixel device, using interband and intersubband transitions in quantum wells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".