Atomic‐Scale Manipulation and In Situ Characterization with Scanning Tunneling Microscopy
Bibliographic record
Abstract
Abstract Scanning tunneling microscope (STM) has presented a revolutionary methodology to nanoscience and nanotechnology. It enables imaging of the topography of surfaces, mapping the distribution of electronic density of states, and manipulating individual atoms and molecules, all at atomic resolutions. In particular, atom manipulation capability has evolved from fabricating individual nanostructures toward the scalable production of the atomic‐sized devices bottom‐up. The combination of precision synthesis and in situ characterization has enabled direct visualization of many quantum phenomena and fast proof‐of‐principle testing of quantum device functions with immediate feedback to guide improved synthesis. Several representative examples are reviewed to demonstrate the recent development of atomic‐scale manipulation, focusing on progress that addresses quantum properties by design in several technologically relevant materials systems. Integration of several atomically precisely controlled probes in a multiprobe STM system vastly extends the capability of in situ characterization to a new dimension where the charge and spin transport behaviors can be examined from mesoscopic to atomic length scale. The automation of atomic‐scale manipulation and the integration with well‐established lithographic processes further push this bottom‐up approach to a new level that combines reproducible fabrication, extraordinary programmability, and the ability to produce large‐scale arrays of quantum structures.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".