IOP PUBLISHINGNANOTECHNOLOGY

Nanotechnology 21 (2010) 145307 (6pp)

doi:10.1088/0957-4484/21/14/145307

Positioning of quantum dots on metallic
nanostructures
R K Kramer1,2 , N Pholchai1 , V J Sorger1, T J Yim1 , R Oulton1 andX Zhang1
1

NSF Nanoscale Science and Engineering Center, University of California, Berkeley, CA,USA
2
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
E-mail: xiang@berkeley.edu

Received 29 September 2009, in final form 8 December 2009
Published 16 March 2010
Online at stacks.iop.org/Nano/21/145307
Abstract
The capability to position individual emitters, such as quantum dots, near metallic
nanostructures is highly desirable for constructing active optical devices that can manipulate
light at the single photon level. The emergence of the field of plasmonics as a means to confine
light now introduces a need for high precision and reliability in positioning any source ofemission, which has thus far been elusive. Placing an emission source within the influence ofplasmonic structures now requires accuracy approaching molecular length scales. In this paper
we report the ability to reliably position nanoscale functional objects, specifically quantum dots,with sub-100-nm accuracy, which is several times smaller than the diffraction limit of aquantum dot’s emission light. Electron beam lithography-defined masks on metallic surfaces
and a series of surface chemical functionalization processes allow the programmed assembly of
DNA-linked colloidal quantum dots. The quantum dots are successfully functionalized to areas
as small as (100 nm)2 using the specific binding of thiolated DNA to Au/Ag, and exploiting thestreptavidin–biotin interaction. An analysis of the reproducibility of the process for various
pattern sizes shows that this technique is potentially scalable to the single quantum dot levelwith 50 nm accuracy accompanied by a moderate reduction in yield.
(Some figures in this article are in colour only in the electronic version)

potential to achieve the necessary molecular length scaleaccuracy and, with it, the prospect of positioning single QDs.
Relevant applications for this technique include integration of
nano-emitters in optical cavities [5–7], nanowires [3, 8] andwaveguides [9, 10].
Existing methods for positioning nano-particles are
elusive given the aforementioned constraints on accuracy.
Bimberg et al [11] reported the fabrication of nanoscale
QDs with good optical properties using a natural-growthmode of strained-layer semiconductors.
The Stranski–
Krastanow growth method produces InAs [12] or CdSe [13]QDs at telecom and visible wavelengths, respectively. One
considerable disadvantage to this technique is the inability tocontrol where QDs form and their size due to the spontaneous
nature of the growth. This problem has been partially solved by
growing the QDs on a surface patterned with nano-sized holes,
which define a strain field in which the QDs form [14–17].
However, this method is only partially compatible withthe growth conditions of the surrounding optical structures.

1. Introduction
In order to exploit the quantum nature of light, it is importantto realize optical systems that can isolate the interaction ofa single electronic transition of a single mode of light. The
emerging field of nano-optics [1] now points to new ways
to strengthen this interaction between light and matter byusing metallic nanostructures to confine light well below its
wavelength [2, 3]. However, placing an emission sourcein the vicinity of such tightly bound optical modes requires
positioning accuracy on molecular length scales, which iscurrently a key problem in nano-optics [4]. While positioning
accuracy remains a challenge, the specific placement of single
quantum dots (QDs) remains a distant goal. In this paper
we report on the ability to successfully position a small
number of QDs on a metallic surface with 100 nm accuracy
and discuss the potential of this method to functionalize
individual dots to 50 nm. The use of chemical functionalizationand specific binding is appealing in this case as it has the0957-4484/10/145307+06$30.00

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Nanotechnology 21 (2010) 145307

Cui et al [18] have reported the use of capillary forces toassemble nano-particles. However, liquid-phase depositionof nanocrystals into grooves using capillary forces requires
specific control of the evaporation rate, liquid–substrate surface
tension and many other parameters which are difficult tocharacterize and reproduce. These approaches all utilize someform of nanoscale patterning along with a force related to thepattern’s configuration. Alternative approaches are emerging
that functionalize the pattern itself, for example with a selfassembled monolayer of molecules which are designed to
specifically bind to the functional object. Wang et al [9]have reported on a QD waveguide device for which fabrication
was demonstrated through DNA-mediated self-assembly on
Si/SiO2 and the smallest functionalized length was 1 μm.
DNA-based hybrid nano-assembly has also been used fornano-particles [19–22], carbon nanotubes [23] and multimetal
microrods [24]. Protein-based methods [25, 26] have also
been reported to show functionalization to metallic surfaces,
but require the underlying substrate to be non-metallic since
removal of the photoresist cannot be accommodated. Also,
almost all previously reported methods are based on bottomup approaches, which intrinsically have higher defects and are
harder to control.
Here, we report the ability to successfully position a small
number of colloidal QDs to within less than 100 nm accuracy
on metallic surfaces via combined top-down fabrication andbottom-up assembly. In this work, a DNA-mediated process
for programmed assembly of QDs [9, 10, 27, 28] has been
adapted to accommodate gold or silver surfaces, i.e. forplasmonic structures. Electron beam lithography (EBL)
was used to create chemical binding sites via predetermined
openings in the photoresist film, thus exposing the underlying
metal surface at predetermined locations. In subsequentassembly steps (further) chemical reactions will be restricted
to these binding sites.
The QD functionalization is carried out with a double
chemical key–lock system. The key feature hereby is singlestranded DNA (ssDNA) providing both the anchor to thesubstrate and selective binding to the QD. While a thiol groupon one end of the ssDNA enables selective binding to thehydrophilic substrate, a biotin group at the other end provides
a docking mechanism for the QD. The QDs used in this study
are covered with a streptavidin layer that has been proven to
selectively bind to the biotin from the ssDNA [9]. The stock
1 μM aqueous solution of streptavidin-conjugated CdSe/ZnS
core–shell QDs (Q10021MP) were obtained from Invitrogen.
Each streptavidin molecule (a four-unit protein) having a sizeof 5 nm adds to the original dimension of CdSe/ZnS QDs(∼5 nm). This method provides for desired QD positioning on
metallic surfaces, which is a critical step in nanoscale optics.

a N2 gun. A 3% dilution of PMMA (A3) was spin-coated,yielding an approximately 90 nm thick layer (4000 rpm for48 s), followed by a solvent dry-out step (180 ◦ C for 20 min).
Standard EBL was carried out to define the binding sites. After
exposure, samples were developed in 1:3 (v:v) methyl isobutyl
ketone (MIBK):IPA (70 s), rinsed with IPA and blown dry
with a N2 gun. It was important to create a hydrophilic metal
substrate to ensure proper binding of the electronegatively
charged thiol group of the ssDNA. A mild Ar-plasma etch
(20 W for 10 s) provides a viable compromise between thedesired surface preparation and parasitic photoresist etching.
Immediately after the plasma treatment the DNA solution(diluted to 50 μM with PBS) was drop-casted onto the samples.
For the following resting step (24 h), samples were placed in ahome-made humidified chamber to ensure proper interaction
between the DNA and the substrate. The DNA (Integrated
DNA Technologies) used in this work was defined by linker
modifications 5 biotin–TEG (triethylene glycol) and 3 thiol
modifier C3 S–S (disulfide). For the first surface chemical
reaction, the DNA thiol modifier C3 S–S (disulfide) allows
the DNA to bind to the gold surface. The gold–sulfide bondis strong and provides the functionality of the self-assembled
monolayer (SAM).
After the resting step, samples were rinsed with 1× PBS
to wash off unbound DNA. Due to the lack of rigidity of
ssDNA, the surface-bound DNA molecules can have manydifferent conformations or entanglements with neighboring
molecules [29], which may suppress the biotin linkers’ affinity
to bind with streptavidin-conjugated QDs. Therefore, samples
were submerged in 6-mercapto-1-hexanol (MCH) (2 μl) andPBS (3 ml) solution (1 min) to enable passivation of the
DNA self-assembly. MCH contains a sulfhydryl (thiol) groupand hydroxyl (alcohol) group at each end of a 6-carbon, or
11.8 Å [30], spacer arm, which forces the ssDNA uprightto perform as a hook for the next chemical reaction. Prior
to the QD functionalization step, samples were post-rinsed
with PBS to remove any excess and unbound DNA and/or
MCH molecules. Streptavidin-coated QDs were drop-casted
on the surface and samples were placed back in the humidified
chamber (t > 30 min). Immersing the entire sample in PBS(5 min) was found to remove any excess QDs and/or otherunbound particles. Finally, the PMMA photoresist layer was
lifted-off with dichloromethane (3 min) followed by drying
the samples in ambient air. Figure 1(a) summarizes theindividual steps for the functionalization process. Testing
the protocol on a flat substrate, we found clear signaturesof successfully functionalized QDs from an atomic force
microscope (AFM) image (figure 1(b)). The measured height(z -direction) of single bumps was found to be about 17 nm,
corresponding well to the QD dimension of 15 nm. In addition,a surface scan of a control sample consisting of a gold (Au) film
atop a silicon (Si) substrate shows a very low surface roughness(<1 nm rms) without the signature of any bump-like features.

2. Fabrication
Substrates were prepared by evaporation of gold or silver onto
a thin adhesion layer (Cr) on a Si-wafer with a user-defined
PMMA pattern written by EBL. The wafers were cleaved
and subsequently washed with xylene, acetone, isopropyl
alcohol (IPA) and de-ionized (DI) water and blown dry with

3. Results
In order to test the size dependency of the programmability,
binding sites, referred to hereafter as patterns, of various sizes
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Nanotechnology 21 (2010) 145307

Figure 2. Functionalization of DNA-directed self-assembled QDs.
(a) Scanning electron microscope image of a 5 × 5 array ofsuccessfully patterned QDs of variously sized square binding sites.
Pattern feature lengths are 4 μm, 2 μm, 1 μm, 500 nm, 250 nm and
100 nm. Inset: a patterned 100 nm square area of functionalized QDs(dashed line indicates the intended binding site). (b) Functionalized
CdSe QDs show photoluminescence under optical excitation(λpump = 488 nm) below the band edge as red glowing areas in a
confocal microscope image. Scale bar: 30 μm.

Figure 1. Two layer assembly of QD patterns. (a) EBL pattern the
PMMA-coated metallic substrate, treat with plasma etch; deposit
thiol and biotinylated DNA, deposit 6-mercapto-1-hexanol (MCH) toact as a spacer and expose biotin groups at the surface; deposit
streptavidin-coated QDs; remove PMMA by immersion indichloromethane. (b) AFM image of sparsely functionalized QDs on
a gold substrate. The surfaces roughness is characterized as
approximately 1 nm, and single QDs are ∼17 nm in diameter.
(c) Control AFM scan of a gold film.

emission of CdSe quantum dots as a bright signal in contrast to
unfunctionalized areas (figure 2(b)). Detailed pattern featuresin this fluorescence image are poorly resolved due to limited
spatial resolution and detection sensitivity of our imaging
system and not protocol limitations. That is, an SEM scanarguably shows sharp functionalized QD features down to thedeep sub-micrometer level (figure 2(a)).
Figure 3(a) verifies a square law behavior for the average
number of QDs functionalized in the various square sizedpatterns. The QD numbers and the√error bars scale with thesquare of the feature length and 1/ n , respectively, where nis the average number of QDs per pattern size. We have useda Matlab® image segmentation program to locate and countthe number of QDs within squares. For a square with an area
greater than (0.5 μm)2 , individual QDs could not be resolved
while viewing the entire square. However, partial images of thesquare up to QD resolution were obtained to gain information
about the QD packing density. The algorithm was developed
to count QDs in randomly selected 100 nm square areas ofa partial image providing an average density output, which
was subsequently multiplied by the total functionalized area of

were fabricated and the assembly process tested. Successful
QD functionalization, ranging from large scale (4 × 4 μm2 )down to small scale (0.1×0.1 μm2 ) binding sites, demonstrates
the scalability and control of the process down to only
a few QDs (inset figure 2(a)). High resolution scanningelectron microscopy (SEM) images reveal individual QD
resolution of the intended binding sites and allow for ananalysis of QD density and quality of the functionalization
process (figure 2(a)). We verified the optical quality offunctionalized QDs by exciting them optically below theband gap energy. Indeed we found the characteristic red
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Nanotechnology 21 (2010) 145307

Figure 3. A (Matlab) algorithm was implemented to analyze scanning electron microscope images of functionalized QDs in differently sizedbinding sites. (a) The total number of QDs for various pattern sizes, which shows quadratic polynomial behavior. (b) Average QD density ofvarious pattern sizes from arbitrarily selected (100 nm)2 areas within each pattern size. The maximum possible QD density was
34 QDs/(100 nm)2 . Average QD density was calculated from a set of ten arbitrarily (random) selected 100 nm square areas within eachpattern size. The error bars represent the span of the collected data. (c) Maximum, minimum and modal (most commonly repeated) QD
densities versus feature length from random (100 nm)2 areas within the central regions of patterns. Inset, (500 nm)2 pattern. (d) QD density
versus pattern length near the edges of the smaller patterns as defined by the outermost dot. Inset, (500 nm)2 pattern. The QD density
increased with reduction of pattern length. Towards the edges, QDs tended to clump together, making the distances between them very small,but QD density was still low. In the innermost region, QDs were more uniformly distributed. (e) Average distance between QDs per patternsizes.

the square. The total functionalized area was estimated froman image of the entire square and characterized by grayscale
numerical pixel value (image segmentation). The algorithm
output an approximation of the total number of functionalized
QDs in the square.

For an analysis of the QD density, arbitrarily selected
100 nm square areas within each pattern size were selected(figures 3(b)–(d)). While the average QD density increased
with increasing pattern size in squares with a feature length
less than 1 μm, larger squares show nearly constant average
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Nanotechnology 21 (2010) 145307

to entanglement and spatial configuration. While the MCH
spacers force the DNA into an upright position, they also
space the DNA apart and suppress the closest packing density.
The effectiveness of the MCH spacers was not quantified inthis report, leaving the possibility of unexposed biotin groups.
Moreover, any variation in the uniformity of charge of theimmobilized DNA would ultimately yield non-uniform QD
binding. Lastly, multiple rinsing steps introduce loss of binding
throughout.

QD densities. Similarly, QD density fluctuations were largest
for the smallest patterns and remained nearly constant acrosslarger pattern sizes. This indicates that for squares with afeature length greater than 1 μm, the QD packing density
is determined by the surface functionalization of Au–thiol–
DNA rather than pattern area. This is consistent with results
for the maximum packing density of the smaller patterns,
which was found to be nearly equal to the maximum packing
density of larger patterns (figure 3(c)). Since the streptavidincoated QDs are approximately 17 nm in diameter (measured
by AFM), the maximum possible packing QD density could be
34 QDs/(100 nm)2 . This yields a maximum packing densityof approximately 50% for all pattern sizes.
With a reduction of pattern size, the photoresist thickness
and edge effects of the EBL process become increasingly
important for the QD packing density. The reduction of themode and minimum QD density value for small patterns shows
statistical limitations potentially brought on by these edge
(fringing) effects (figure 3(c)). To further investigate this, analgorithm was executed to analyze the relative configurationof dots in different regions of the smaller ( Apattern < 4 μm2 )QD-functionalized patterns (figure 3(d)). The algorithm
determined the number of dots and distances between nearestneighboring dots in ten arbitrarily selected 100 nm squareareas with close proximity to both the center and edges ofthe patterns (figures 3(c) and (d)). As shown by figure 3(d),functionalization in the smallest squares was more uniform
than functionalization close the edge of slightly larger squares.
Near the edges, QDs tended to clump together, yielding small
distances between dots, yet the density of dots was still
relatively low. Near the middle of the patterns, QDs were more
uniformly distributed and distances between dots were moreconsistent. Figure 3(e) shows the average distance between
QDs for entire patterns.
Notice that functionalization appears to have improved
slightly with reduction in pattern size. Specifically, figure 3(d)shows that functionalization of QDs in 100 nm squares was
slightly better than functionalization close the edges of otherlarger squares. An explanation of this phenomenon is notdefinite, yet we hypothesize that in larger squares, the edgesof the pattern never come into contact with the drop-casted
solutions due to surface tension of the liquid. Smaller squares(with an increased aspect ratio) may have the added benefitof a capillary action to draw solution in towards the substrate.
Alternatively, reducing the pattern area, the size of a square
may simply define a radius of curvature for an inset liquid
droplet, causing the actual functionalized area to be circularrather than square, which is consistent with the images infigure 2. While this eliminates binding in the corners of thepatterns, it provides for good binding at the center.
The functionalization process reported here strives fora monolayer of uniform and well dispersed QDs, yet QD
density appears to be limited by constraints of the surface
chemical process (more so than pattern size). Firstly, the DNA
packing density was limited by the effectiveness of the thiolbinding to the Au surface with enhanced hydrophilicity of thesurface by the plasma treatment. Once bound, the DNA may
not diffuse and organize a monolayer on the Au surface due

4. Conclusion
In summary, control over small numbers (∼10) of nanoscale
functional objects (QDs) to within less than 100 nm accuracy
has been demonstrated. We have introduced a self-assembly
fabrication method for positioning colloidal QDs on metallic
substrates. Highly discriminatory binding sites for QDs were
defined by electron beam lithography and a DNA-mediatedprocess. Testing QD functionalization in variously sizedbinding sites yielded a maximum packing density of 50%
for all pattern sizes. Edge effects and statistical limitations
became more apparent with reduction of pattern size, yet
the highest achievable packing density was consistent for all
pattern sizes. This suggests that the chemical functionalization
limited the packing density of QDs more than the pattern size,and denotes the potential for fabricating single QDs in small
patterns with high accuracy. With this demonstrated potential
to increase control over the positioning of a small numberof functionalized nano-particles, we can envision nano-opticaldevices that utilize QDs as optical emitters, thus allowing thefrontier of optics research to access molecular length scales.

Acknowledgments
The authors thank the National Science Foundation (NSF)
NSEC (DMI-0327077). R Kramer thanks the NSF Graduate
Fellowship Program for financial support.
Work wasperformed in part at the University of California, Berkeley
Microfabrication Laboratory and we hereby acknowledge theWelch Foundation.

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