Bridging the gap between electron and optical microscopy through neural network-enabled training and imaging
Bibliographic record
Abstract
Obtaining high-resolution images using an optical microscope is critical when dealing with micro/nanoscale objects. Current techniques use high magnification objective lenses with high numerical apertures to resolve closely spaced objects at the micron/nanoscale. However, these lenses often require additional optics and have a narrow depth of field, preventing ease of use. To date, scanning electron microscopy (SEM) is used for imaging beyond the diffraction limit and has led to various breakthroughs in semiconductor physics and nanotechnology. An alternative to an SEM is using artificial intelligence (AI) to enable super-resolution techniques with correlated image sets. We utilize a convolutional neural network (CNN) and generative adversarial network (GAN) to train correlated images gathered from higher magnification SEM and lower magnification SEM, resulting in a model that enables resolving nanoscale features. We demonstrated that by training a neural network with SEM images, we are able to aid the optical microscope to image beyond the diffraction limit with a resolution closer to the SEM.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".