Chin. Phys. Lett.  2012, Vol. 29 Issue (12): 128401    DOI: 10.1088/0256-307X/29/12/128401
CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Independently Tunable Multichannel Filters Based on Graphene Superlattices with Fractal Potential Patterns
ZHANG Hui-Yun, ZHANG Yu-Ping**, GAO Ying, YIN Yi-Heng
Qingdao Key Laboratory of Terahertz Technology, College of Science, Shandong University of Science and Technology, Qingdao 266510
Cite this article:   
ZHANG Hui-Yun, ZHANG Yu-Ping, GAO Ying et al  2012 Chin. Phys. Lett. 29 128401
Download: PDF(747KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract An independently tunable multichannel filter based on graphene superlattices with fractal potentials is theoretically studied. It is found that such fractal structures with a defect layer possess an unusual tunneling state occurring inside the forbidden gap, and the defect modes can be modulated by changing the width of the defect layer. The facts that the wave functions of defect states do not overlap and their bases are orthogonal lead to the result of the independency among the defect modes. The modulation of energy, energy interval and number of the defect modes may lead to potential applications in graphene-based electronic devices.
Received: 27 April 2012      Published: 04 March 2013
PACS:  84.30.Vn (Filters)  
  73.21.Cd (Superlattices)  
  73.40.Gk (Tunneling)  
TRENDMD:   
URL:  
https://cpl.iphy.ac.cn/10.1088/0256-307X/29/12/128401       OR      https://cpl.iphy.ac.cn/Y2012/V29/I12/128401
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
ZHANG Hui-Yun
ZHANG Yu-Ping
GAO Ying
YIN Yi-Heng
[1] Novoselov K S, Geim A K, Morozov S V, Jiang D, Zhang Y, Dubonos S V, Grigorieva I V and Firsov A A 2004 Science 306 666
[2] Novoselov K S, Geim A K, Morozov S V, Jiang D, Katsnelson M I, Grigorieva I V, Dubonos S V and Firsov A A 2005 Nature (London) 438 197
[3] Castro Neto A H, Guinea F, Peres N M R, Novoselov K S and Geim A K 2009 Rev. Mod. Phys. 81 109
[4] Young A F and Kim P 2009 Nat. Phys. 5 222
[5] Bolotin K I, Ghahari F, Shulman M D, Stormer H L and Kim P 2009 Nature (London) 462 196
[6] Wang F, Zhang Y, Tian C, Girit C, Zettl A, Crommie M and Shen Y R 2008 Science 320 206
[7] Xu X G, Zhang C, Xu G J and Cao J C 2011 Chin. Phys. B 20 027201
[8] Lin X, Wang H L, Pan H and Xu H Z 2011 Chin. Phys. B 20 047302
[9] Wang S X, Li Z W, Liu J J and Li Y X Z 2011 Chin. Phys. B 20 077305
[10] Jiang H T, Zhang J F, Wang Z L, Li Y H and Chen H 2012 Phys. Lett. A 376 1509
[11] Chen Y H 2009 Appl. Phys. B 95 757
[12] Chen X and Tao J W 2009 Appl. Phys. Lett. 94 262102
[13] Zhang H Y, Gao Y, Zhang Y P, Xu S L and Wang S F 2011 Appl. Phys. Lett. 99 072108
[14] Wang L G and Zhou S Y 2010 Phys. Rev. B 81 205444
[15] Meyer J C, Girit C O, Crommie M F and Zettl A 2008 Appl. Phys. Lett. 92 123110
[16] Marchini S, Gunther S and Wintterlin J 2007 Phys. Rev. B 76 075429
[17] Pan Y, Zhang H, Shi D, Sun J, Du S, Liu F and Gao H J 2009 Adv. Mater. 21 2777
[18] Shao S W, Chen X S, Lu W, Li M and Wang H Q 2007 Appl. Phys. Lett. 90 211113
Related articles from Frontiers Journals
[1] QU Hua, MA Wen-Tao, ZHAO Ji-Hong, CHEN Ba-Dong. Kernel Least Mean Kurtosis Based Online Chaotic Time Series Prediction[J]. Chin. Phys. Lett., 2013, 30(11): 128401
Viewed
Full text


Abstract