InAs Nanowires Grown by Metal–Organic Vapor-Phase Epitaxy (MOVPE) Employing PS/PMMA Diblock Copolymer Nanopatterning

Huang, Yan; Kim, Tae Wan; Xiong, Shenglin; Mawst, Luke J.; Kuech, T. F.; Nealey, Paul F.; Dai, Yushuai; Wang, Zihao; Guo, Wei; Forbes, David V.; Hubbard, Seth M.; Nesnidal, M.

doi:10.1021/nl403163x

Cited by 16 publications

(16 citation statements)

References 31 publications

(58 reference statements)

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“…In comparison, nanoimprint lithography (NIL) has emerged as a promising candidate for high-throughput and low-cost patterning for nanowire array growth [75][76][77][78][79], offering full wafer processing and industrial compatibility [80]. Other techniques with potential benefits have been utilized for nanowire growth, such as deep-UV lithography [81], laser interference lithography [82,83], nanosphere lithography [68,84] and block copolymer lithography [85].…”

Section: Substrate Patterningmentioning

confidence: 99%

Towards high efficiency nanowire solar cells

2017

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Section: Substrate Patterningmentioning

confidence: 99%

Towards high efficiency nanowire solar cells

2017

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“…Metal organic chemical vapor epitaxy (MOVPE ) is predominantly used for III-V nanowire growth as initial results of GaAs nanowire solar cell grown using VLS showed very low efficiencies, less than 1% [295]. Though multiple reports on InP nanowire growth using MOVPE existed [320][321][322][323], the first InP nanowire solar cell, based on the method was reported in 2009, and showed an impressive PCE of 3.37% [324]. In the case of GaAs, it was not until 2012, that an impressive PCE of 2.54% was reported in densely packed uniform GaAs nanowires [301].…”

Section: Fabrication Techniquesmentioning

confidence: 99%

“…SEM image of MOVPE grown lnAs nanowires. Reprinted with permission from[323]. Copyright (2013) American Chemical Society.…”

mentioning

confidence: 99%

Nanostructured photovoltaics

2019

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In this review, we begin by discussing the need for harnessing renewable energy resources in the context of global energy demands. A summary of firstand second-generation solar cells, their efficiency and grid parity is provided, followed by the need to reduce material and installation costs, and achieve higher efficiencies beyond the Shockley-Queisser limit imposed on single junction cells. We also discuss the specific advantages offered by nanomaterials in enhanced energy harvesting, what design platforms comprise nanostructured photovoltaics, and list the prominent categories of nanomaterials used in the design of third generation solar cells. We review the significant nanostructured photovoltaic platforms that have encouraging power conversion efficiencies, have the potential for long term stability (both structural and functional) and have received attention in the field. In addition to their operational principle, we highlight both their advantages and shortcomings, along with insights into possible improvements. We include alternate routes to improving power conversion efficiency, not by tuning material properties to match the solar spectrum but using additives and/or structural modifications to allow more efficient harvesting of sunlight, either by reducing losses or by altering the spectral properties. The properties of nanomaterials that make them well-suited as active materials in photovoltaic devices (broadband absorption, high quantum yield, etc.) also make them ideal candidates for luminescent solar concentrators (LSCs). These devices also harvest solar energy, but instead of directly allowing charge generation, they act as downconverters for other photovoltaic cells. We review dye, thin film, and quantum dot based LSCs that have garnered a lot of attention in recent years as these devices face a resurgence given the advances in materials science and engineering which have led to novel quantum dots and hybrid semiconductors. The review ends with where the future of nanostructured photovoltaics is headed, what device designs and materials development are needed to achieve efficiencies beyond the Shockley-Queisser limits and fulfil the goal of the 3rd and 4th generation photovoltaics.

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“…11 This combination of self-assembly with lithography is termed directed self-assembly (DSA) and has been primarily directed towards applications in microelectronics, including memory storage materials, 6,12,13 finFET, 5,14,15 and vias. [16][17][18] These nanopatterned substrates have also seen use as catalysts for growth of ordered nanowire arrays, [19][20][21] as a platform for protein detection, 22,23 separation membranes, [24][25][26][27] surface enhanced Raman spectroscopy (SERS) substrates, [28][29][30] anti-reflective coatings in photovoltaics, [31][32][33] and chemical and biomedical sensors. [34][35][36][37] The self-assembly of a monolayer of a given BCP on a flat, featureless surface results in a polycrystalline morphology with uncorrelated nano-to micron-sized domains, with a significant concentration of defects.…”

Section: Introductionmentioning

confidence: 99%

Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches

Ginige

Song

Olsen

et al. 2021

ACS Appl. Mater. Interfaces

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Self-assembly of block copolymers (BCP) is an alternative patterning technique that promises sublithographic resolution and density multiplication. Defectivity of the resulting nanopatterns remains too high for many applications in microelectronics, and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapour pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flowcontrolled system is combined with Design of Experiments (DOE) and machine learning (ML) approaches. The SVA flow-controlled system enables precise optimization of the conditions of self-assembly of the high Flory-Huggins interaction parameter (c) hexagonal dot array-forming BCP, PS-b-PDMS. The defects within the resulting patterns at various length scales are then characterized and quantified. The results show that defectivity of the resulting nanopatterned surfaces are highly dependent upon very small variations of the initial film thicknesses of the BCP, as well as the degree of swelling under the SVA conditions. These parameters also significantly contribute to the quality of the resulting pattern with respect to grain coarsening, as well as the formation of different macroscale phases (single and double layers, and wetting layers). The results of qualitative and quantitative defect analyses are then compiled into a single figure of merit (FOM) using DOE and ML approaches, which enable identification of the narrow region of optimum conditions for SVA for a given BCP. The result of these analyses is a faster and less resource intensive route towards the production of low defectivity BCP dot arrays via rational determination of the ideal combination of processing factors. The DOE and machine learning-enabled approach is generalizable to scale-up of self-assembly-based nanopatterning for applications in electronics microfabrication.

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InAs Nanowires Grown by Metal–Organic Vapor-Phase Epitaxy (MOVPE) Employing PS/PMMA Diblock Copolymer Nanopatterning

Cited by 16 publications

References 31 publications

Towards high efficiency nanowire solar cells

Towards high efficiency nanowire solar cells

Nanostructured photovoltaics

Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches

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