Tailoring Single Photon Sources in Hexagonal Boron Nitride via Chemical Vapor Deposition and Nanoscale Focused Ion Beam Milling
Bibliographic record
Abstract
Emerging quantum information technologies demand robust, tunable, single photon sources. Solid-state single photon emitters (SPEs) in the two-dimensional material hexagonal boron nitride (hBN) offer unique advantages, including stability and integration potential, yet current fabrication methods lack precise control over the emitter placement and properties. In this work, we demonstrate a high-yield approach to patterning SPE arrays in hBN by combining focused ion beam (FIB) milling with chemical vapor deposition (CVD) of nanocrystalline graphitic carbon. Using statistical design and analysis of experiments, we systematically map a high-dimensional parameter space─spanning FIB exposure and CVD conditions─to identify the optimal regimes for SPE formation and tunability. Our method leverages widely available fabrication tools and provides critical insights into defect activation mechanisms, offering a scalable, reproducible path toward controllable quantum emitter synthesis. Beyond hBN, this approach opens the door to generating defect-based SPEs in other low-defect solid-state materials. The result is a practical and versatile platform for creating quantum light sources tailored for applications in communication, sensing, and computation.
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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) | 0.000 | 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".