Data from: Photovoltage field-effect transistors
Bibliographic record
Abstract
It is of intense interest to extend the excellent performance of silicon photodetectors into the infrared spectrum beyond silicon’s bandgap. Detection of infrared radiation is at the base of applications such as night vision, health monitoring, and optical commuincations. Silicon is the workhorse of modern electronics, but its electronic bandgap prevents detection of light at wavlengths longer than ~ 1100 nm. Here we present the photovoltage field effect transitor (PVFET) that uses silicon for charge transport, but adds infrared sensitization via a quantum dot light absorber. Using the photovoltage generated at the silicon:quantum dot heterointerface, combined with the high transconductance provided by the silicon device, we demonstrate high gain (>104 electrons/photon at 1500 nm), fast time response (< 10 s), and widely tunable spectral response. The PVFET shows a responsivity 5 orders of magnitude higher at 1500 nm wavelength than prior IR-sensitized silicon detectors. The sensitization is achieved using a room temperature solution process and does not rely on traditional high temperature epitaxial growth semiconductors, as per germanium and III-V compounds. Our results demonsrate, for the first time, colloidal quantum dots as an efficient platform for silicon based infrared detection, competitive with state-of-the-art epitaxial semiconductors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.066 |
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".