Bibliographic record
Abstract
Semiconducting nanowires and nanostructures have been a focus of much research in the past decade. Most often the aim is to understand how their properties can lead to novel device applications predicated on their smaller volumes and therefore potentially interesting quantum size effects. If the crystal structure and composition is under control, correlations with electronic transport, optoelectronic emission, and other properties rely on the formation of well-understood electrical contacts. While much is known about planar contacts to semiconductor thin films and quantum wells, the reduction in volume and increase in surface area of nanowires has led to many fascinating questions and discoveries. This special issue of Semiconductor Science and Technology brings together a collection of work in this field. Each paper provides another example of how nanoscale geometries are influencing reaction kinetics and interfacial transport mechanisms. Orrù et al describe experimental results for alloyed NiGeAu contacts to GaAs nanowires where an axial Schottky barrier is formed instead of the expected ohmic contacts familiar for planar geometries. The formation of transparent indium tin oxide ohmic contacts to GaAs nanowires—important to nanowire optoelectronic devices—is described in a paper by Zhang et al . Simulations of the significant nanoscale effects on transport in Mo contacts to GaAs is the subject of a paper by Aldegunde et al . Tang et al review the case of Ni reactions for contacts to III–V, Si and Ge nanowires developing an understanding of the general effects of nanoscale geometries on reaction kinetics and contact properties. Blanchard et al focus on ohmic contacts to GaN nanowires establishing an upper limit on contact resistivity with direct 4-point probe measurements. A comparison of the ohmic properties of metal contacts to carbon nanotubes as a function of geometry, sidewall versus end, is the focus of a paper by Wilhite et al . Conclusions are reached for the large differences that are observed between the two types of contacts. Hihath and Tao describe the problem of forming reliable molecular scale junctions with practical suggestions for future approaches. The issue provides a timely update on progress in this intriguing field. We thank the IOP editorial staff, in particular Ben Sheard, for their support, and all contributors for their efforts in making this special issue possible.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.019 |
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".