File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Nano-robot enabled characterizations of local electrical properties for nano-structures

TitleNano-robot enabled characterizations of local electrical properties for nano-structures
Authors
KeywordsElectrical Characterization
Nano-robot
Augmented Reality
Atomic Force Microscopy (AFM)
Issue Date2012
Citation
Proceedings of the IEEE Conference on Nanotechnology, 2012 How to Cite?
AbstractLocal electrical characterization has wide spectrum of applications in various areas. However, there are a number of difficulties that hinder the precise measurement of local electrical properties of samples, particularly those within nano-scale spatial resolution. Inspired by these challenges, we developed a nano-robot enabled electrical characterization system that can be utilized to pinpoint the local electrical properties of materials, devices, and bioentities with high spatial and electrical resolution. This system consists of an electrical characterization unit and a nano-robot with an augment reality system, which was developed from a traditional atomic force microscopy (AFM). The augment reality system provides real-time visual feedback. The real-time visual display integrated with the real-time force feedback from the nano-robot allows a precise control of the position and force of the AFM tips towards samples, which are significant for the sensitivity of local electrical measurement. The system design and implementation are presented in the paper. Experiments were carried out to study the local conductance of a multi-wall carbon nanotube (MWCNT), demonstrating the effectiveness of this system. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/213275
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChen, Hongzhi-
dc.contributor.authorXi, Ning-
dc.contributor.authorSong, Bo-
dc.contributor.authorYang, Ruiguo-
dc.contributor.authorLai, King W C-
dc.contributor.authorChen, Liangliang-
dc.contributor.authorQu, Chengeng-
dc.date.accessioned2015-07-28T04:06:44Z-
dc.date.available2015-07-28T04:06:44Z-
dc.date.issued2012-
dc.identifier.citationProceedings of the IEEE Conference on Nanotechnology, 2012-
dc.identifier.issn1944-9399-
dc.identifier.urihttp://hdl.handle.net/10722/213275-
dc.description.abstractLocal electrical characterization has wide spectrum of applications in various areas. However, there are a number of difficulties that hinder the precise measurement of local electrical properties of samples, particularly those within nano-scale spatial resolution. Inspired by these challenges, we developed a nano-robot enabled electrical characterization system that can be utilized to pinpoint the local electrical properties of materials, devices, and bioentities with high spatial and electrical resolution. This system consists of an electrical characterization unit and a nano-robot with an augment reality system, which was developed from a traditional atomic force microscopy (AFM). The augment reality system provides real-time visual feedback. The real-time visual display integrated with the real-time force feedback from the nano-robot allows a precise control of the position and force of the AFM tips towards samples, which are significant for the sensitivity of local electrical measurement. The system design and implementation are presented in the paper. Experiments were carried out to study the local conductance of a multi-wall carbon nanotube (MWCNT), demonstrating the effectiveness of this system. © 2012 IEEE.-
dc.languageeng-
dc.relation.ispartofProceedings of the IEEE Conference on Nanotechnology-
dc.subjectElectrical Characterization-
dc.subjectNano-robot-
dc.subjectAugmented Reality-
dc.subjectAtomic Force Microscopy (AFM)-
dc.titleNano-robot enabled characterizations of local electrical properties for nano-structures-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/NANO.2012.6322154-
dc.identifier.scopuseid_2-s2.0-84869191642-
dc.identifier.eissn1944-9380-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats