Effects of vertical strain on zigzag graphene nanoribbon with a topological line defect

2015 ◽  
Vol 67 ◽  
pp. 116-120 ◽  
Author(s):  
Li-Hua Qu ◽  
Jian-Min Zhang ◽  
Ke-Wei Xu ◽  
Vincent Ji
Keyword(s):  
Graphene Nanoribbon ◽  
Line Defect ◽  
Vertical Strain

Effects of local shear strain on the zigzag graphene nanoribbon with a topological line defect

2020 ◽  
Vol 123 ◽  
pp. 114195
Author(s):  
Li-Hua Qu ◽  
Xiao-Long Fu ◽  
Chong-Gui Zhong ◽  
Jian-Min Zhang
Keyword(s):  
Shear Strain ◽  
Graphene Nanoribbon ◽  
Line Defect ◽  
Local Shear

A graphene quantum dot realized by an armchair graphene nanoribbon with line defect

2013 ◽  
Vol 7 (8) ◽  
pp. 579-582 ◽  
Author(s):  
Xiao-Yan Sui ◽  
Zhi-Chao Li ◽  
Wei-Jiang Gong ◽  
Guo-Dong Yu ◽  
Xiao-Hui Chen
Keyword(s):  
Quantum Dot ◽  
Graphene Nanoribbon ◽  
Line Defect ◽  
Graphene Quantum Dot ◽  
Armchair Graphene Nanoribbon ◽  
Armchair Graphene

Line-defect–induced Fano interference in an armchair graphene nanoribbon

2013 ◽  
Vol 103 (1) ◽  
pp. 18003 ◽  
Author(s):  
W. J. Gong ◽  
X. Y. Sui ◽  
L. Zhu ◽  
G. D. Yu ◽  
X. H. Chen
Keyword(s):  
Graphene Nanoribbon ◽  
Line Defect ◽  
Fano Interference ◽  
Armchair Graphene Nanoribbon ◽  
Armchair Graphene

Strain-induced ferromagnetism in zigzag edge graphene nanoribbon with a topological line defect

2012 ◽  
Vol 86 (12) ◽  
Author(s):  
Ting Hu ◽  
Jian Zhou ◽  
Jinming Dong ◽  
Yoshiyuki Kawazoe
Keyword(s):  
Graphene Nanoribbon ◽  
Line Defect ◽  
Zigzag Edge

In-Line Defect to Bitmap Signature Correlation: A Shortcut to Physical FA Results

2000 ◽  
Author(s):  
Julie Segal ◽  
Arman Sagatelian ◽  
Bob Hodgkins ◽  
Tom Ho ◽  
Ben Chu ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Integrated Circuit ◽  
Failure Mechanisms ◽  
Memory Devices ◽  
Line Defect ◽  
Yield Improvement ◽  
Electrical Test ◽  
Large Numbers ◽  
Improvement Process ◽  
Existing Data

Abstract Physical failure analysis (FA) of integrated circuit devices that fail electrical test is an important part of the yield improvement process. This article describes how the analysis of existing data from arrayed devices can be used to replace physical FA of some electrical test failures, and increase the value of physical FA results. The discussion is limited to pre-repair results. The key is to use classified bitmaps and determine which signature classification correlates to which type of in-line defect. Using this technique, physical failure mechanisms can be determined for large numbers of failures on a scale that would be unfeasible with de-processing and physical FA. If the bitmaps are classified, two-way correlation can be performed: in-line defect to bitmap failure, as well as bitmap signature to in-line defect. Results also demonstrate the value of analyzing memory devices failures, even those that can be repaired, to gain understanding of defect mechanisms.

Crosstalk bandwidth and stability analysis in graphene nanoribbon interconnects

2015 ◽  
Vol 55 (8) ◽  
pp. 1262-1268 ◽  
Author(s):  
Amin Bagheri ◽  
Mahboubeh Ranjbar ◽  
Saeed Haji-Nasiri ◽  
Sattar Mirzakuchaki
Keyword(s):  
Stability Analysis ◽  
Graphene Nanoribbon
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