Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Michael Caputo
michael.vislab@gmail.com

Last updated: June 15, 2016

 

 

Inspection and Quality Control

High-Throughput Large-Area Identification and Quality Control of Graphene

Practical applications of graphene require a reliable high-throughput method of graphene identification and quality control, which can be used for large-scale substrates and wafers. We have proposed and experimentally tested a fast and fully automated approach for determining the number of atomic planes in graphene samples. It is based on an original image processing algorithm, which utilizes micro-Raman calibration, light background subtraction, lighting non-uniformity correction, and color and grayscale image processing for each pixel. Our approach works for various substrates and can be applied to mechanically exfoliated, chemically derived, deposited or epitaxial graphene on an industrial scale.

Large-Scale Automated Identification and Quality Control of Exfoliated and CVD Graphene via Image Processing Technique

Graphene, a monolayer of carbon atoms, is a high-interest material in the research community and semiconductor industry due to its extraordinary electronic, thermal, and mechanical properties. Graphene layer identification is very important since its intrinsic properties change drastically between each 0.34-nm thick layer. Current methods of identification rely on restrictive small-area microscopy techniques, the most robust being micro-Raman spectroscopy. Here we present a new method for a large-area graphene layer identification characterized by low cost, high accuracy, high throughput, complete automation, and scalability. Our metrology tool is based on a fast image processing algorithm, which analyzes optical contrasts between single-layer, bi-layer, and few-layer graphene used for exfoliated, transferred, or grown graphene flakes on large wafers verified by micro-Raman spectroscopy.