Dual‐Surface Reaction Enabled Organic–Inorganic Hybrid Photodiodes for Faint Light Detection and Imaging
Bibliographic record
Abstract
Abstract Organic–inorganic heterojunction photodetectors are garnering significant research interest. The surface‐sensitive nature of nanometer‐thin single‐crystalline semiconductors vis‐à‐vis device performance provides an ideal platform for studying organic contacts. Herein, organic‐Si nanomembrane (SiNM) hybrid photodetectors are demonstrated for probing faint light and self‐powered imaging. Universal approaches to controlling dual‐surface reactions are proposed to achieve high‐performance devices. A 1,4,5,8,9,11‐hexaazatriphenylene hexacarbonitrile (HAT‐CN) organic layer effectively blocks electron injection current by reacting to unpin the SiNM surface Fermi level. A low work‐function ytterbium oxide (YbO x ) (≈2.9 eV) buffer reduces the hole injection. A facile surface reaction process is systematically optimized for HAT‐CN and ytterbium with SiNM to selectively consolidate and deoxidize the natively grown silicon oxide (SiO x ), respectively, thereby minimizing noise. Contact interfaces between various dielectrics are investigated for maximizing device performance. The devices achieve competitive performance among state‐of‐the‐art organic–inorganic photodetectors, notably the low noise (sub‐pA/µm), ultra‐fast microsecond response speed and a high rectification of 3 × 10 7 . Further, an imaging sensor is demonstrated to operate in self‐powered mode. These results provide key insights into interfaces, devices, and system‐level applications in organic–inorganic heterogeneous optoelectronics.
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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".