Microscale Contacts for Nanowire Characterization Using Microscope Projection Photolithography
Bibliographic record
Abstract
Microscope projection photolithography (MPP) offers a versatile method of prototyping microscale devices. The benefits of MPP include the ability to create features at a variety of dimensions, the ability to work outside of a clean room, and the ability to use robotic microscope stage controls to adjust the positions of features easily and accurately. This makes MPP a precise, yet flexible technique ideal for manufacturing contact pads that can be used to investigate the properties of individual nanowires. An enormous breadth of research has been dedicated to the synthesis and characterization of nanowires. The properties of individual nanowires can, however, vary greatly between syntheses or within a batch. Characterization of individual nanowires remains an important step in the scale-up of these materials. Their characterization does, however, often require complex and highly sensitive self-assembly techniques to arrange nanowires on substrates with proper placement of electrical contacts. Some work has been done on techniques for the selective characterization of individual nanowires, but those methods rely on using focused electron-beam writing techniques to connect nanowires to pre-existing electrical contacts. The study of nanowires would benefit from a rapid, flexible approach to fabricating contacts for individual nanowires. This study demonstrates a system for fabricating electrical contacts with minimum feature sizes of ∼0.9 μm on individual nanowires (e.g., diameters from <75 to >125 nm) that are positioned randomly on a small substrate (e.g., <1 cm 2 ). Designs for double and quadruple contact pads have been shown to enable the effective measurement of the electrical properties of individual nanowires. The techniques provided herein can provide a rapid, simple, and customizable method of studying individual nanowires.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".