Silicon-integrated monocrystalline oxide–nitride heterostructures for deep-ultraviolet optoelectronics
Bibliographic record
Abstract
New opportunities for high-performance CMOS-compatible optoelectronic devices have accelerated the interest in vertically configured device topologies that enable next-generation photonic technologies. Lately, TiN has been identified as a promising refractory metal–ceramic for the hybrid integration of emerging semiconductor materials on a variety of substrates, including Si, MgO, and sapphire. Among these, Si is the least expensive and most commonly used element and substrate material in the semiconductor device industry. Following these examples, a hybrid oxide–nitride–Si stack is proposed and thoroughly investigated herein for its potential use in DUV optoelectronic device applications. The stack comprises β-Ga2O3 thin films grown heteroepitaxially on TiN/Si platforms, wherein the TiN interlayers were heteroepitaxially grown on bulk (100)-oriented Si and act as lattice-mismatched templates and bottom device electrodes. Albeit the relatively large lattice mismatch between Si and TiN, a low in-plane rotation of 3 ∘ revealed that the TiN layers continued to grow as a bulk crystal, paving the way for heteroepitaxial β-Ga2O3 thin films being grown without exhibiting amorphous and metastable phases. DUV photodetectors based on this optoelectronic heterostructure exhibited average peak spectral responsivity and external quantum efficiency levels as high as 249 A/W and 1.23 × 105%, respectively, in the ultraviolet-C regime at an illuminating power density of around 12 µW/cm2.
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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".