Quantum Dot Photodiodes for Enhanced Short-Wavelength Infrared Photodetection: Engineering Charge Transport
Bibliographic record
Abstract
Short-wavelength infrared (SWIR) light detection is becoming increasingly important in depth-sensing, spectroscopy, and night vision. Since SWIR radiation is transparent to silicon — the workhorse of visible photodetection — epitaxial compound semiconductors are instead the dominant SWIR technology. However, the process of epitaxial growth on crystalline substrates increases fabrication cost, and this has, to date, limited the breadth of application of SWIR technologies.Colloidal quantum dot (CQD) photodiodes are an emerging alternative to enable low-cost, high-quality SWIR detection. They benefit from a tunable bandgap, high absorption, and solution-based fabrication. A typical CQD photodiode consists of an active absorber layer of ligand-capped CQDs sandwiched between an electron transport layer (ETL) and a hole transport layer (HTL). In this work, I show that addressing the need for improved charge transport in PbS (lead sulphide) CQD photodiodes improves stability and response time. Prior to the work presented in this thesis, PbS CQD photodiodes suffered from limited stability: photocurrent degraded to <10% of maximum within 2 hours of operation. I showed that the zinc oxide nanoparticles of the ETL cause this instability. These nanoparticles promote charge trapping and oxygen adsorption. I developed a new ETL with 10x lower oxygen binding energy to achieve devices with stability exceeding 120 hours.In the HTL, poor charge transport affects sensor response time rather than stability. Low-mobility organic-ligand-capped CQDs are conventionally used as HTLs. Consequently, response times exceed 800 ns. I developed an improved mobility HTL, based on nickel oxide, that enabled devices with quantum efficiency and dark current similar to the best previous devices; but with response time reduced fourfold to ~200 ns. These devices nevertheless retained an extended temporal decay tail which persisted for >10 µs. Such a tail of response corresponds to lag. I studied the effect of active layer ligand concentration on decay time, finding that ligand concentration has a strong influence on the final trap density and thus temporal response. I was able to reduce this lag effect fourfold while maintaining low dark current and high quantum efficiency. Overall, stability and response time of SWIR PbS CQD photodiodes were enhanced via layer-wise charge transport improvements.
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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".