Automated nanomanipulation for nanodevice construction
Bibliographic record
Abstract
Nanowire field-effect transistors (nano-FETs) are nanodevices capable of highly sensitive, label-free sensing of molecules. However, significant variations in sensitivity across devices can result from poor control over device parameters, such as nanowire diameter and the number of electrode-bridging nanowires. This paper presents a fabrication approach that uses wafer-scale nanowire contact printing for throughput and uses automated nanomanipulation for precision control of nanowire number and diameter. The process requires only one photolithography mask. Using nanowire contact printing and post-processing (i.e. nanomanipulation inside a scanning electron microscope), we are able to produce devices all with a single-nanowire and similar diameters at a speed of ~1 min/device with a success rate of 95% (n = 500). This technology represents a seamless integration of wafer-scale microfabrication and automated nanorobotic manipulation for producing nano-FET sensors with consistent response across devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".