Multiple parameter dynamic photoresponse microscopy for data-intensive optoelectronic measurements of van der Waals heterostructures
Bibliographic record
Abstract
Quantum devices made from van der Waals (vdW) heterostructures of two dimensional (2D) materials may herald a new frontier in designer materials that exhibit novel electronic properties and unusual electronic phases. However, due to the complexity of layered atomic structures and the physics that emerges, experimental realization of devices with tailored physical properties will require comprehensive measurements across a large domain of material and device parameters. Such multi-parameter measurements require new strategies that combine data-intensive techniques-often applied in astronomy and high energy physics-with the experimental tools of solid state physics and materials science. We discuss the challenges of comprehensive experimental science and present a technique, called Multi-Parameter Dynamic Photoresponse Microscopy (MPDPM), which utilizes ultrafast lasers, diffraction limited scanning beam optics, and hardware automation to characterize the photoresponse of 2D heterostructures in a time efficient manner. Using comprehensive methods on vdW heterostructures results in large and complicated data sets; in the case of MPDPM, we measure a large set of images requiring advanced image analysis to extract the underlying physics. We discuss how to approach such data sets in general and in the specific case of a graphene-boron nitride-graphite heterostructure photocell.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".