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Research Papers: Design Automation

Characterization and Design of Functional Quasi-Random Nanostructured Materials Using Spectral Density Function

[+] Author and Article Information
Shuangcheng Yu

Mechanical Engineering,
Northwestern University,
2145 Sheridan Road,
Evanston, IL 60208
e-mail: shuangchengyu2012@u.northwestern.edu

Yichi Zhang

Mechanical Engineering,
Northwestern University,
2145 Sheridan Road,
Evanston, IL 60208
e-mail: yichizhang2013@u.northwestern.edu

Chen Wang

Mechanical Engineering,
Northwestern University,
2145 Sheridan Road,
Evanston, IL 60208
e-mail: chenwang2015@u.northwestern.edu

Won-kyu Lee

Material Science and Engineering,
Northwestern University,
2145 Sheridan Road,
Evanston, IL 60208
e-mail: wonkyulee2017@u.northwestern.edu

Biqin Dong

Biomedical Engineering,
Northwestern University,
2145 Sheridan Road,
Evanston, IL 60208
e-mail: dongbq@northwestern.edu

Teri W. Odom

Chemistry,
Northwestern University,
Evanston, IL 60208;
Material Science and Engineering,
Northwestern University,
2145 Sheridan Road,
Evanston, IL 60208
e-mail: todom@northwestern.edu

Cheng Sun

Mechanical Engineering,
Northwestern University,
2145 Sheridan Road,
Evanston, IL 60208
e-mail: c-sun@northwestern.edu

Wei Chen

Mechanical Engineering,
Northwestern University,
2145 Sheridan Road,
Evanston, IL 60208
e-mail: weichen@northwestern.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 9, 2016; final manuscript received April 13, 2017; published online May 10, 2017. Assoc. Editor: James K. Guest.

J. Mech. Des 139(7), 071401 (May 10, 2017) (12 pages) Paper No: MD-16-1431; doi: 10.1115/1.4036582 History: Received June 09, 2016; Revised April 13, 2017

Quasi-random nanostructures are playing an increasingly important role in developing advanced material systems with various functionalities. Current development of functional quasi-random nanostructured material systems (NMSs) mainly follows a sequential strategy without considering the fabrication conditions in nanostructure optimization, which limits the feasibility of the optimized design for large-scale, parallel nanomanufacturing using bottom-up processes. We propose a novel design methodology for designing isotropic quasi-random NMSs that employs spectral density function (SDF) to concurrently optimize the nanostructure and design the corresponding nanomanufacturing conditions of a bottom-up process. Alternative to the well-known correlation functions for characterizing the structural correlation of NMSs, the SDF provides a convenient and informative design representation that maps processing–structure relation to enable fast explorations of optimal fabricable nanostructures and to exploit the stochastic nature of manufacturing processes. In this paper, we first introduce the SDF as a nondeterministic design representation for quasi-random NMSs, as an alternative to the two-point correlation function. Efficient reconstruction methods for quasi-random NMSs are developed for handling different morphologies, such as the channel-type and particle-type, in simulation-based microstructural design. The SDF-based computational design methodology is illustrated by the optimization of quasi-random light-trapping nanostructures in thin-film solar cells for both channel-type and particle-type NMSs. Finally, the concurrent design strategy is employed to optimize the quasi-random light-trapping structure manufactured via scalable wrinkle nanolithography process.

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References

Figures

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Fig. 1

Optimized periodic nanostructure for light trapping in a thin-film solar cell [4,5]

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Fig. 2

Biological and man-made quasi-random functional nanostructures [13,14,16,18]

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Fig. 3

Comparison of spectral density functions and two-point correlation functions for representing quasi-random nanostructures

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Fig. 4

Gaussian random field modeling for quasi-random nanostructure reconstruction based on spectral density function

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Fig. 5

Demonstration of quasi-random nanostructure reconstruction based on different spectral density functions using Gaussian random field modeling

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Fig. 6

Random close packing algorithm of disk particle for quasi-random nanostructure reconstruction based on spectral density function

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Fig. 7

Demonstration of quasi-random nanostructure reconstruction based on different spectral density functions using particle packing algorithm

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Fig. 8

Concurrent structure and processing-design methodology for quasi-random nanostructured material systems

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Fig. 9

Computational design of quasi-random light-trapping nanostructure using spectral density function for single incident wavelength

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Fig. 10

Results of computational design of quasi-random light-trapping nanostructure using spectral density function over broadband

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Fig. 11

(a) A wrinkle-based quasi-random nanostructure with wrinkle wavelength of 180 nm and (b)–(d) the spectral density functions of three different wrinkle patterned nanostructures with wrinkle wavelengths of 180 nm, 450 nm, and 2000 nm

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