Engineering Semiconductor Nanostructures for Short-Wave Infrared Detection
Bibliographic record
Abstract
Photodetectors fabricated using silicon and III-V compound semiconductors enable accurate light detection across the visible and infrared spectra. These, however, suffer from limited wavelength selectivity and from manufacturing costs that prohibit wider application.Solution-processed semiconductor nanostructures such as colloidal quantum dots (CQDs) represent a promising alternative. These combine ease of processing and a bandgap that is tunable across the short-wave infrared (SWIR) spectrum. At the outset of this study, CQD photodetectors sensitive to this spectral region suffered from high dark current (10-2 mA.cm-2), poor stability (a few hours), and low detection speed (fall time of 500 ns). In this thesis, I focus on the realization and investigation of new CQD device architectures and their effects on dark current, stability, and speed. I then explore the potential benefits of CQD photodetectors over epitaxial technologies. First I investigate the limitations of prior CQD photodetectors. I find that inadequate control over the band alignment and surface passivation lead to high dark current. I pursue the development of a surface-ligand engineering strategy that tailors the functionalities of each layer, enabling me to achieve record low dark current (10-3 mA.cm-2). Next, I study two performance-limiting factors: operating stability and speed. I examine a chief cause of photodetector instability – oxygen adsorption by the electron transport layer – and develop a synthesis method with 10x lower binding energy to oxygen. The new photodetectors are stable for over 100 hours. I then develop a device model to quantify the impact of charge transport layers on the detection speed and use this to fabricate 3x faster photodetectors (fall time of 150 ns). Finally, I explore the advantages of CQD photodetectors over traditional epitaxial semiconductors. I tune the spectral response of the CQDs to a SWIR band in which atmospheric absorption suppresses solar illumination. This results in CQD photodetectors achieving 140x higher signal-to-background ratio compared to Si-based photodetector systems. I then explore the integration of CQDs onto lattice-mismatched substrates, developing an interfacial layer that improves the injection of carriers into silicon. The studies herein offer routes to improve CQD photodetector performance and demonstrate the benefits of CQDs over epitaxial technologies.
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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".