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© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
SUPPLEMENTARY INFORMATIONDOI: 10.1038/NPHOTON.2017.75
NATURE PHOTONICS | www.nature.com/naturephotonics 1
Supplementary Materials for
Broadband image sensor array based on graphene-CMOS integration
Stijn Goossens1,*, Gabriele Navickaite1,*, Carles Monasterio1,*, Shuchi Gupta1,*, Juanjo Piqueras1, Raul Perez1,
Gregory Burwell1, Ivan Nikitskiy1, Tania Lasanta1, Teresa Galan1, Eric Puma1, Alba Centeno3, Amaia Pesquera3, Amaia Zurutuza3, Gerasimos Konstantatos1,2,†, Frank Koppens1,2,†
1 ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels
(Barcelona), Spain. 2 ICREA – Institució Catalana de Recerça i Estudis Avancats, Lluis Companys 23, Barcelona, Spain.
3Graphenea SA, 20018 Donostia-San Sebastian, Spain * These authors contributed equally to this work
correspondence to: [email protected] , [email protected]
Supplementary Methods
ROIC post processing
Graphene channel fabrication
The post processing of the ROIC die involves a transfer of a sheet of CVD grown graphene
on copper (supplied by Graphenea S.A. and cut to the size of the ROIC die) using a wet
transfer process followed by pixel patterning using a photoresist mask and reactive ion
etching using an argon and oxygen plasma. Contact patterning and metallization was done
using a lift-off process. Figure S1 shows a Raman map of the resulting graphene pixels.
PbS QD layer deposition
The synthesis of PbS nanocrystals was carried out under inert conditions using a Schlenk line
as previously described in literature.1,2 The final PbS oleate-capped nanocrystals were
dispersed in toluene for device fabrication. The PbS layers were spin casted in a layer-by-
layer process followed by a ligand exchange with EDT (1,2-ethanedithiol).
Prototype digital camera description
The graphene-quantum dot digital camera was operating at room temperature in
ambient conditions. The ROIC die was first bonded to a LCC chip carrier and clamped into a
custom rig that provides the electrical connections to a control system that comprises the
following blocks (Figure S2):
(1) Power supply unit to provide external bias voltages, including readout circuitry and pixel
supplies;
(2) Digital sequencing unit to generate all clock and control signals responsible for the sensor
timing, e.g. array row selection, exposure time definition, or shutter operation; and
(3) 12-bits digitizer to convert the sensor output signal to digital values.
these three blocks are controlled by an embedded computer via a PXI (PCI eXtensions for
Instrumentation) bus, and interface the image sensor through a dedicated PCB board. The
camera allows operating and reading out the image sensor at 50 frames per second.
The user interface is directly connected to the embedded computer, which runs both
acquisition and analysis software packages. The acquisition software is written in C# while
all analysis is performed using Python. Both embedded computer and PXI modules are off-
the-shelf components from National Instruments.
Image capturing
The visible image Fig 2c was captured in reflection mode by illuminating a target
image with a desklamp fitted with a 6.5 W 3000K LED bulb. The lamp was directed to a
picture placed in front of the lens. A f/2.8 objective of 50 mm focal length was used to
project the image on the image sensor. The irradiance behind the objective was ~1·10-4
W/cm2.
The objects for the infrared images (Fig 2b,d,e,g,i) were illuminated with a 1000W,
3200K incandescent lamp yielding an irradiance behind the objective of ~10-4 W/cm2. We
placed a 1100nm long pass filter in the optical path for the images that were taken purely
with infrared light. The lamp was directed to 3 dimensional objects placed in front of the lens.
A f/2.9 objective of 75 mm focal length was used to project the image on the image sensor.
The image capture process consists of collecting a sequence of ~100 frames at ~14
fps. As the pixel resistance uniformity and drift were not optimized, we used a chopper to
modulate the light at 1 Hz which enabled us to obtain the dark current of the pixels, hence the
signal we plot is dV=Vout,light - Vout,dark. To correct for photo-response non-uniformity we
obtained a white reference map by placing a RESTAN target or a blank paper at the end of an
image capturing sequence.
In Figure S3 we summarize the detailed setup for capturing each of the images in
Figure 2.
A stage fog machine generated the fog in Figure 2e,f. This fog is much denser than
outdoor fog. The silicon wafer that we used in figure 2g,h was standard, low-doped silicon.
Data processing
First we corrected drift for the captured time sequence per pixel by selecting two
periodic points, fitting a line and subtracting that line from the data. Then we performed a
FFT to obtain the power spectral density (PSD) versus frequency of the signal. From the PSD
we extracted the square root of the level at 1 Hz for each pixel. We performed this for both
the image and the white reference data. By calculating dVimage/dVwhite for each pixel we
corrected for light response non-uniformities. Pixels with dVimage > dVwhite were marked as
non-working. Those pixels were patched with an infilling algorithm that takes the mean of
the surrounding, working pixels. Finally we performed a median filter to get rid of salt and
pepper noise.
Optical system description
The power dependencies in Figs. 3d and 4d in the main text were measured by
attenuating the light of a fiber-coupled laser with a digital variable attenuator before it was
coupled into an integrating sphere that uniformly illuminates the image sensor (or reference
detector). In between the image sensor and the integrating sphere we placed a chopper. For
the visible image sensor we used a 633 nm fiber coupled diode laser source and for the
infrared image sensor a fiber coupled super continuum laser with AOTF module. The data
acquisition was performed in the same way as for the image capturing.
For the acquisition of the spectra in Figs. 4a,b in the main text a NKT supercontinuum
laser with AOTF module was used. The laser was fiber-coupled to a collimator (0.98 mm
beam diameter) that was directly illuminating the sample. Between the collimator and the
device a chopper was placed. For each wavelength we acquired a time sequence of frames
from which we extracted dV.
Reference photodetectors
Reference photodetectors were made on standard n++ Si / 285 nm SiOx substrates
using a wet transfer technique of CVD graphene (obtained from Graphenea S.A.) followed by
pixel patterning using a photoresist mask and etching with an argon and oxygen plasma.
Contact patterning and metallization was done using a lift-off process. The quantum dots and
quantum dot layer application details are the same as for the ROIC process (see above).
Night-glow measurement
For the night-glow measurement shown in Fig. 4C of the main text, we used a
reference photodetector with an area of 1 mm². The device was mounted in a setup without
lens, operating at room temperature in ambient conditions. The detector was pointing to the
sky at an angle of ~30 degrees. For obtaining a high signal-to-noise ratio we modulated the
incoming light at 9.9 Hz with a chopper and read the light signal using a lock-in technique
based on a software FFT. We used long-pass filters on top of the detectors for selecting
spectral bands. In Figure S4 we show a schematic layout of the setup
Supplementary Notes
Signal path image sensor
In Figure S5 we show the full functional diagram of the image sensor. From the
control circuitry we can operate the VDD , VSS and VREF voltages. In practice we used the
controllable compensating resistor for tuning the operating regime for each pixel.
Optimization of pixel conductance
Controlling the pixel dark conductance is essential to provide a proper matching
between photosensitive and blind pixels, which maximizes the photo-signal amplitude at the
imager output. Besides, homogeneous dark conductance within pixels is required to achieve
good spatial uniformity. Figure S6 shows how the dark signal of a pixel varies when the
pixel resistance changes, by tuning the compensating resistor. The optimum operating point
corresponds to the case when the output dark signal is 0V. A change of ±1 kΩ around the
optimum value reduces the photo-signal by more than 50%.
Electro-optical characterization
Resistance maps
We have fabricated different ROIC dies. In Figure S7 we show resistance data
obtained from three different ROIC dies covered with graphene and colloidal quantum dots.
Die 1 was used to obtain the images shown in the main text in Fig. 2c. As we transferred the
CVD graphene sheets by hand on the ROIC dies, alignment was not always perfect, hence the
pixelated (active) area of the ROIC was partially (>85%) covered with graphene.
Yield calculation
The image sensor has in total 111,744 pixels. When characterizing the image sensor,
we detect pixels that have a conductive graphene channel by sweeping the Rcomp resistor value
and recording Vout. If Vout crosses 0 V the graphene channel is conductive. This is the case for
94,983 pixels for the visible and NIR image sensor, which gives a graphene coverage of 85%.
A large part of the 15% of non-working pixels can be attributed to the area that is not covered
with graphene. If we select a rectangular area of 255 x 345 (87,975) pixels within the area
that is covered with graphene we find 87,834 pixels that have a Vout that crosses 0 V.
Therefore, the yield of the area with transferred graphene is 99.8 %, close to unity.
Noise characterization
We performed a noise analysis of the graphene quantum dot hybrid photodetectors by
recording a time trace of the current under constant source-drain bias in the dark. Taking the
Fourier transform of the time trace yields a typical noise spectrum as plotted in Fig. S8 with a
1/frequency dependence. Moreover, we find that the noise scales linearly with source-drain
bias and inversely with the area of the graphene channel. These observations are typical for
1/f noise in graphene 3.
To make a proper comparison to the pixels in the image sensors, we show in Fig. S8 a
noise spectrum obtained from a reference hybrid graphene quantum dot photodetector with a
similar area (308 µm2) as the pixels in the ROIC (255 µm2). At 1 Hz, the normalized power
spectral density SI/I2=6·10-10 Hz-1. The factor ß = (SI/I
2)(W*L) (defined at f=10 Hz) was
introduced by Stolyarov et al. 4. We observe ß = 1.8·10-8 µm2/Hz. Improvements in device
processing can reduce the 1/f noise such that lower noise equivalent irradiances can be
obtained. In graphene encapsulated with hBN ß=5·10-9 µm2/Hz has been observed 4.
To filter out the majority of 1/f noise, a lock-in scheme detection can be implemented:
the higher the modulation frequency, the lower the noise. The data presented in Fig. 3D in the
main text in purple was obtained using a lock-in detection with a modulation at 100 Hz,
yielding an NEI of 9·10-10 W/cm2 for a pixel of 384 µm2. The measurement of the night glow
(Fig. 4C in the main text) was performed at a lock-in frequency of 9.9 Hz. In an image sensor
however, a lock-in scheme is not practical and a broadband read–out is most often used. This
system integrates all noise up to the cut-off frequency of the read-out system: the area under
the data in Fig. S8.
Performance summary
In Table S1 we summarize the performance parameters extracted from Fig 3D and 4D
in the main text. We remark that the graphene-QD reference detectors exhibit a response time
below 1 ms, and this time response can in principle also be achieved for the imager, by
optimizing the design.
Performance projection
In Table S2 we calculated the noise equivalent irradiance (NEI) and specific
detectivity for various cases of an optimized graphene pixel read-out system and compared
those values to commercially available silicon CMOS and InGaAs image sensors. First for
modulation-type read-out which involves modulating the light signal and also for a standard
broadband amplifier system. We assumed 1/area scaling of the 1/f noise in graphene, which
we also verified experimentally 3. The NEI and detectivity were calculated for our sensors at
a read-out speed of 60 fps. We can improve the mobility of graphene (and hence the signal of
the detector) by more than a factor 10 by optimizing the substrate of the graphene (hBN for
example5).
The projected performance is comparable to the most recently available InGaAs
cameras. Those are not monolithic CMOS and are thus high cost (>15kE). Comparing to
uncooled InGaAs the projected performance of the graphene quantum dot hybrid imagers is
better. Especially for λ>1700 nm, the region where only extended InGaAs operates, the
detectivity of the graphene quantum dot hybrid sensors is at least an order of magnitude
better without the need of a power consuming four-stage thermo-electric cooler.
Pixel size and fill factor
The image sensors used for obtaining the data represented in the main text were based
on a graphene pattern that was optimized for the specific ROIC but sub-optimal in terms of
fill factor. In Figure S9a we show a schematic of the pixel design. As the source and drain
contacts in the off-the-shelve ROIC were placed in the corners and the resistance of the
graphene needed to be in the range 20-100 kΩ we patterned the graphene in an s-shape. The
fill factor for the image sensors used in the paper was less than 35%.
In a custom ROIC design we can increase the fill factor to almost 100%. For the large
pixel in Figure S9b the fill factor is 95%. Moreover, we can decrease the pixel size to 3x3
µm2 by relying on state of the art sub 100 nm precision lithography processes. For the design
in Figure S9c the fill factor is 93%.
References
1. Mihi, A., Beck, F. J., Lasanta, T., Rath, A. K. & Konstantatos, G. Imprinted Electrodes
for Enhanced Light Trapping in Solution Processed Solar Cells. Adv. Mater. 26, 443–
448 (2014).
2. Ip, A. H. et al. Hybrid passivated colloidal quantum dot solids. Nat. Nanotechnol. 7,
577–582 (2012).
3. Balandin, A. A. Low-frequency 1/f noise in graphene devices. Nat. Nanotechnol. 8,
549–55 (2013).
4. Stolyarov, M. A., Liu, G., Rumyantsev, S. L., Shur, M. & Balandin, A. A. Suppression
of 1/f noise in near-ballistic h-BN-graphene-h-BN heterostructure field-effect
transistors. Appl. Phys. Lett. 107, 23106 (2015).
5. Banszerus, L. et al. Ultrahigh-mobility graphene devices from chemical vapor
deposition on reusable copper. 1–6 (2015).
6. Tissot, J.-L. et al. High-performance uncooled amorphous silicon video graphics array
and extended graphics array infrared focal plane arrays with 17-μm pixel pitch. Opt.
Eng. 50, 61006 (2011).
7. Sony IMX377 CMOS image sensor. (2016). Available at: http://www.sony-
semicon.co.jp/products_en/IS/sensor2/img/products/IMX377CQT_ProductSummary_
v1.5_20150414.pdf.
8. Andanta FPA-640x512-TE2 InGaAs imager. (2012). Available at:
http://www.andanta.de/pdf/andanta_fpa640x512-te2.pdf.
9. Sensors Unlimited Micro-SWIR 640CSX Camera. Available at:
http://www.sensorsinc.com/images/uploads/documents/640CSX_commercial.pdf.
10. Theuwissen, A. J. P. CMOS image sensors: State-of-the-art. Solid. State. Electron. 52,
1401–1406 (2008).
Supplementary Figures
Figure S1 Raman map of the 2D-peak intensity (2682 cm-1) of an area of the ROIC with
patterned graphene on top (before depositing colloidal quantum dots).
Digitizer
Power Supply Unit
Digital Sequencing Unit
PXI embedded Computer
PXI BUS
PXI BUS
PXI BUS
IF PCB
Image Sensor
Figure S2 Block diagram of the camera prototype (optics not included).
Illumination 1 kW 3200 K
incandescent lamp
with 1100 nm long
pass filter
1 kW 3200 K
incandescent lamp
6.5 W 3000K LED
bulb
1 kW 3200 K
incandescent lamp
with 1100 nm long
pass filter
Object Apple and pear Box of apples Paper with image
Lena printed in
black and white
Glass with water
Lens system f/2.9, f=75 mm,
single lens
f/2.9, f=75 mm,
single lens
f/2.8, f=50 mm,
objective
f/2.9, f=75 mm,
single lens
Image sensor -
object distance
[cm]
100 100 60 100
Irradiance on
image sensor
[W/cm2]
1·10-4 1·10-4 1·10-4 1·10-4
Image sensor
wavelength
range [nm]
300-1850 300-1850 300-1000 300-1850
Figure S3 Overview of experimental details for obtaining the different images. The rightmost column
represents the setup for images e, g and i.
Figure S4 Schematic layout of the night-glow measurement setup. The image on the right is taken of
the sky with a standard silicon image sensor.
...Analog
Output(s)
AnalogOutput(s)
Capacitive TransImpedance
Amplifier
Sampling & Hold
BLIND PIXEL
Co
mp
ensa
tin
gR
esis
tor
Co
lum
n S
elec
tor
CDSCDS
Correlated Double
Sampling
OutputDriver
VREF
VSS
VDD
Row select
ACTIVE PIXELS (x288)
x388
Figure S5 Image sensor functional diagram. The active pixel is switched in a row-by-
row fashion. The two transistors in the circuit regulate the internal resistor biasing6.
Figure S6. Variation of a typical pixel’s output voltage as a function of the pixel dark
resistance, obtained by scanning an internal compensating resistor placed in series with the
photosensitive pixels inside the ROIC chip (Rcomp). The horizontal axis refers to resistance variation
with respect to a dark value of 28 kΩ.
1. S-shaped graphene 2. Meander shaped
graphene
3. Meander shaped
graphene
Bef
ore
ca
lib
rati
on
Aft
er c
ali
bra
tio
n
Res
ista
nce
his
tog
ram
s
Figure S7 Pixel resistance maps for three different ROIC dies with graphene. In the upper row we plot the
resistance before calibration with the compensation resistor (Rpixel) and in the middle row the values after
calibration (Rpixel + Rcomp). In grey we plot the pixels that do not show conductance. The bottom row represents
histograms for the different ROIC dies before (red) and after calibration (blue). The graphene channels in the
pixels of the ROIC dies in column 2 and 3 were etched in a meander shape to obtain larger channel resistance
values as is visible from the median values in the histograms (40 and 55 kOhm). For ROIC 3 the calibration was
not as effective, because the devices suffered from hysteresis.
Figure S8 Normalized noise power spectral density for a reference hybrid graphene quantum dot
photodetector on a n++ Si, 285 nm SiOx substrate sensitized with quantum dots with an exciton peak
of 1670nm (area=308 µm2, Vbackgate=0 V, Vsd=0.1 V) The graphene pixels of the ROIC used for Figure
2C,D in main text are 288 µm2. The dashed line has a slope of -1, showing the 1/f nature of the noise
in the device. The grey zone is the total noise for a read-out system with a bandwidth of 10 Hz . The
noise for a lock-in measurement at a modulation frequency of 10 Hz is indicated with the vertical
arrow.
Column
Row
1 388
1
288
Rpix
el [
k
]
5
10
15
20
25
30
35
40
Column
Row
1 388
1
288
Rpix
el [
k
]
10
20
30
40
50
60
70
80
Column
Row
1 388
1
288
Rpix
el [
k
]
10
20
30
40
50
60
70
80
Column
Row
1 388
1
288
Rpix
el [
k
]
0
5
10
15
20
25
30
35
40
Column
Row
1 388
1
288
Rpix
el [
k
]
0
10
20
30
40
50
60
70
80
Column
Row
1 388
1
288
Rpix
el [
k
]
0
10
20
30
40
50
60
70
80
10 15 20 25 30
500
1000
1500
2000
2500
3000
3500
Resistance [k]
Counts
Rpixel
Rpixel
+ calibrated Roffset
10 20 30 40 50 60 70 80
500
1000
1500
2000
2500
3000
3500
Resistance [k]
Counts
Rpixel
Rpixel
+ calibrated Roffset
50 100 150
500
1000
1500
2000
2500
3000
3500
Resistance [k]
Counts
Rpixel
Rpixel
+ calibrated Roffset
Gr substrate Gr substrateGraphene
So
urc
e
Dra
in
1 µm
35
µm
1 µm
35 µm
Gr substrateGraphene
So
urc
e
Dra
in
100 nm
3 µ
m3 µm
100 nm
Source
Drain
35 µm
Current designDesign with
optimized fill factorScaled down pixel
35
µm
a b c
Figure S9 Different pixel designs. Please note that the different elements in the drawings are not to
scale.
Supplementary Tables
Parameter Units Graphene-QD CMOS imager
Graphene-QD
reference detector
Graphene-QD CMOS imager
Graphene-QD reference detector
Exciton peak nm 920 1670 1580
Illumination nm 633 1670 1550
Pixel size (WxL) µm2 35x35 48x8 35x35 1000x1000
NEI W/cm2 < 10-8 9·10-10 <8·10-7 10-9
Detectivity D* Jones >6·1010 4·1012 >6·108 > 1012
Dynamic Range dB >55 > 80 >32 > 80
Response time ms 100 ms (limited by
ROIC design)
10 ms 100 ms (limited by
ROIC design)
< 1 ms
Table S1. Summary of imager and reference detector performance.
Gr-QD CMOS Modulation-type
read-out
Gr-QD CMOS Broadband read–out
Silicon CMOS
High performance
InGaAs
non-CMOS
Typical InGaAs
non-CMOS
Extended InGaAs non-CMOS
Parameter Units standard substrate, uncooled, small pixel
optimized substrate, uncooled, small pixel
standard substrate, uncooled, small pixel
optimized substrate, uncooled, small pixel
Uncooled, Small pixel
pitch, smartphone
type
Cooled, Sensors unlimited Micro SWIR 640CSX,
Uncooled, large pixel size
4 stage thermo-electric cooler, large pixel size
Wavelength
range
nm 300-2500 300-1100 700-1700 700-1700 1000-2500
Pixel pitch µm <3 <3 12.5 12.5 30
Power con-
sumption
[mW] t.b.d. (for the current 100k Pixel ROIC it was 211 mW)
400 7 3258 (uncooled)-2500(packaged and cooled)9
85·103
Pixel fall
time
ms <1 <1E-4 <1E-4 <1E-4 <1E-4
Quantum
efficiency
% >50 >50 >65 >65 >65
Dynamic
Range
dB >80 <80 68 68 68
NEI* W/cm2 3·10-10 <2·10-11 2·10-9 <4·10-10 6·10-10 2.1·10-10 6·10-9 6.2·10-9
Detectivity Jones 1·1013 * >5·1013 * 6·1011 * >9·1012 * 4·1013 2.8·1013 4·1012 6·1010
Table S2 Performance projection for an optimized Graphene quantum dot CMOS image sensor. The
specific wavelength for which the values are given is 1550nm except for the silicon CMOS . We
compared the performance to a state of the art Si CMOS image sensor that can be found in
smartphones10, to a non-ITAR SWIR camera that is manufactured by Sensors Unlimited9 and to non-
cooled InGaAs and cooled extended InGaAs camera pixels (Xenics Xeva 2.5). *detectivity and NEI at
60 fps, for photoconductive detectors the NEP is determined by the built-in time constant of the
detectors.