This paper presents a scalable and ultrasensitive

frequency-shift magnetic biosensing array scheme. The

theoretical limit of the sensor noise floor is shown to be

dominated by the phase noise of the sensing oscillators. To

increase the sensitivity, a noise suppression technique, Correlated Double Counting (CDC), is proposed with no power overhead. As an implementation example, a 64-cell sensor array is designed in a standard 65nm CMOS process. The CDC scheme achieves an additional 6dB noise suppression. The magnetic sensing capability of the presented sensor is verified by detecting micron size magnetic particles with an SNR of 14.6dB for a single bead and an effective dynamic range of at least 74.5dB

Hajimiri, Ali

Kosai, Shohei

Sideris, Constantine

Wang, Hua

Caltech Authors - Main

An Ultrasensitive CMOS Magnetic Biosensor Array with Correlated 
Double Counting Noise Suppression 
Hua Wang, Shohei Kosai, Constantine Sideris, and Ali Hajimiri 
California Institute of Technology, Pasadena, California, 91125, USA 
Abstract - This paper presents a scalable and ultrasensitive 
frequency-shift magnetic biosensing array scheme. The 
theoretical limit of the sensor noise floor is shown to be 
dominated by the phase noise of the sensing oscillators. To 
increase the sensitivity, a noise suppression technique, Correlated 
Double Counting (CDC), is proposed with no power overhead. As 
an implementation example, a 64-ceII sensor array is designed in 
a standard 65nm CMOS process. The CDC scheme achieves an 
additional 6dB noise suppression. The magnetic sensing 
capability of the presented sensor is verified by detecting micron 
size magnetic particles with an SNR of 14.6dB for a single bead 
and an effective dynamic range of at least 74.5dB. 
Index Terms - Biomedical transducers, biomagnetics, 
biomedical measurements, medical diagnosis, CMOS integrated 
circuits, integrated circuit noise. 
I. INTRODUCTION 
Portable microarrays are promising for Point-of-Care (POC) 
medical applications, such as disease detection, control, and 
monitoring, where the key technical challenges are hand-held 
portability, high sensitivity, battery-level power consumption, 
and low cost. 
Magnetic biosensors in the form of portable microarrays are 
proposed to replace or augment conventional fluorescent 
sensors, which need bulky and expensive optical systems. 
However, all magnetic sensors reported thus far require 
external bias magnetic fields and/or complicated post­
processing, limiting their form factor and cost [1-3]. An 
ultrasensitive frequency-shift sensing scheme has been thus 
proposed to address these issues [4]. In this paper, the 
fundamental noise floor of this sensing scheme is modeled, 
and a noise suppression technique, Correlated Double 
Counting (CDC), is proposed and implemented which 
achieves 6dB of noise reduction without any power overhead 
in the sensor array presented in this paper. 
This paper is organized as follows: Section II introduces the 
frequency-shift magnetic sensing scheme and proposes the 
noise suppression technique. Section III presents a CMOS 
sensor array implementing the technique. The sensor's noise 
suppression performance is characterized in section IV and its 
magnetic sensing capability is verified in section V. 
II. SENSING SCHEME AND NorSE SUPPRESSION TECHNIQUE 
Magnetic sensing is typically accomplished by a sandwich 
bioassay. The target molecules are first captured by pre­
deposited molecular probes onto the sensor surface. Magnetic 
978-1-4244-7732-6/10/$26.00 ©201 0 IEEE 616 
particles (labels) coated with other types of probes are then 
added and immobilized by the captured target molecules. 
Therefore, one can detect the presence of target molecules in 
the sample by sensing the magnetic labels left on the surface. 
A. Frequency Shift Magnetic Sensing and Noise Analysis 
In the frequency-shift sensing scheme, the inductor of an 
on-chip LC oscillator functions as the sensing core. The AC 
current through the inductor generates a magnetic field to 
polarize the present magnetic particles. This increases the total 
magnetic energy in the space and thus leads to an effective 
increase in the sensing inductor inductance. Consequently, the 
on-chip LC oscillator senses this inductance increase via a 
corresponding downshift in oscillation frequency [4]. 
: Step 1. Introduce: 
: Samples : 
Target: : 
........... Molecule : : , , , , 
/�� {)� 
Interference : : 
MOleCUle : : 
sa�::es : : 
Step 2. Wash: Step 3. Add : Step 4. Wash 
and Sense 1 Ma�gnetlc partlcleS l an:':::
t
�
c
e 
: : Particles 
I Magnetic I / � ! Partlcl •• � ! M:�:::�. 
, , /  , , , , 
i i r-........ Jt..., 
: : L..O!!! .... .... 
Fig. 1. Inductive frequency shift magnetic sensing scheme 
The oscillation frequency is measured by frequency 
counting (i.e. recording the number of rising edges within a 
given counting time T). Therefore, the noise floor is set by the 
accumulated jitter at of the waveform which is determined by 
its phase noise spectrum, S</>(w). The relative frequency error 
(Ilf I fa in frequency counting can thus be calculated as 
2 (!J.f)2 at 1 2 aMlfo = T = T2 = T2W� E{[¢(T) - ¢(O)] } 
+00 
- 4 f . 2 [WT] - rrw�T2 S</>(w) Sin T dw, 
o 
(1) 
where fa = wo/Zrr is the oscillation frequency and w is the 
offset frequency for the SSB phase noise S</>(w)[5]. 
(�f)2represents the frequency uncertainty during counting. 
At a short counting time (T « Zrr/wilf3, where wilf3 is the 
lfJ3 comer frequency of the phase noise S</>(w», 1/f2 noise 
dominates the jitter and (Ilf Ifa is inversely proportional to T: 
IMS 2010 
a-f K2T K2 
attlto = T2 = T2 = T' (2) 
where K is the 1/[2 jitter coefficient for the oscillator. For a 
large counting interval (T» ZIT/Wllf3), l/P phase noise 
dominates and leads to (fit/fa independent of T: 
2 _ af _ �2T2 _ 2 af,f/to - T2 - ---:rz - � , (3) 
where ( is the 1/[3 jitter coefficient [5]. Moreover, due to the 
uncertainty principle (effect of finite counting window length), 
there exists a measurement error proportional to 1/ NT2. The 
total frequency uncertainty can be plotted, which shows that 
(2 determines the ultimate sensor noise floor (Fig.3). 
In practical sensor designs, differential sensing is used by 
pairing a sensing oscillator with a reference oscillator [4]. 
Their frequency difference is taken as the sensor output to 
eliminate common-mode drift due to effects, such as supply 
and temperature variations. In differential sensing, an extra 
factor of 2 appears in ait/fa for noise power doubling, 
+00 
attlto = :W�:2 J SrjJ(w) sin2 [�T] dw. 
o 0 
1/fo2T2 
(due to Uncertainty @/ Principle) 
� \ Noise Floor 2�2 �\ (duet01/f' 
��, Phase Noise) 
\�\"'-� 
Noise Floor After 
Noise Cancellation 
Technique 
Noise Power 
Reduction Ratio 
(NPRR) 
(4) 
Fig. 3. Noise floor for oscillator based frequency shift sensing. 
B. Correlated Double Counting (CDC) Noise Suppression 
Technique 
Based on the analysis above, l/P phase noise (captured by 
the (2 coefficient) sets the minimum sensor noise floor. This 
1/ P phase noise is determined by the flicker noise of the 
devices and the waveform properties of the oscillators and is 
generally difficult to reduce beyond a certain level. Based on 
(3), the 1/P noise jitter (�2T2) accumulates at a faster rate 
due to its long correlation time. While this correlation results 
in a higher integrated noise power, it also creates a possibility 
of noise cancellation. 
In a normal differential sensing, the jitters are uncorrelated 
between the sensing and the reference oscillator, which gives 
the extra factor of 2 in (4). However, if the 1/ P phase noise 
is correlated between the two oscillators, this correlated noise 
can be subtracted by a correlated double counting (CDC) 
scheme, where subtracting one frequency count from the other 
will reduce the correlated noise component and thus lowers 
the measurement uncertainty floor as shown in Fig. 3. Since 
978-1-4244-7732-6/10/$26.00 ©201 0 IEEE 617 
1/ P phase noise is mainly from flicker noise upconversion of 
the active devices in a CMOS oscillator, this suggests 
designing the two oscillators in a way to increase the flicker 
noise correlation between the reference and sensing cores, e.g. 
active core sharing, during differential operation (FigA). 
Normal Differential Scheme I Proposed CDC Scheme 
��-- -- -- ---- : �� - -- -- ----
:fsense � : fRet� : :fsense � : fRet� 
\. " ,1, " , - --- --- - I - --- --- -
� �i � : � I 
foutput= fSense- fRet 
, fSense , fRef , ... 
T T 
Fig. 4. Normal differential sensing scheme and CDC scheme. 
This noise suppression technique is analogous to the 
Correlated Double Sampling (CDS) often used in image 
sensors. This proposed CDC scheme for our sensor does not 
increase the power consumption or chip area. Moreover, this 
CDC scheme with oscillator based frequency-shift sensing 
provides a general sensor design methodology which can be 
applied to any type of sensor that measures LC resonant 
changes, e.g. capacitive sensing in pressure sensors [6]. 
The proposed scheme can be modeled mathematically. 
Assuming ¢(t) as the correlated random phase of the 
oscillator waveform, the effective noise for CDC scheme is: 
atf/fo = TL2 E H¢(2T) - ¢(T) - [¢(T) - ¢(O)]n o 
+CIl 
= rr�;T2 J SrjJ(w) sin4 [�T] dw, 
o 
(5) 
where T is the counting time and Wo is the nominal oscillation 
frequency. From (4) and (5), noise shaping functions for 
normal differential scheme and CDC scheme can be defined 
and plotted (Fig. 5). Compared with the normal differential 
case, the CDC provides a 4th order zero at low frequency 
offsets enabling a greater suppression on the 1/[3phase noise. 
1 E + 2 .-----:- ----:-=-:---.-:--. 
<II c: - Normal Differential 
.� � 1 E+1 l..--_-_C_D....: C _ S,-c,- h..;.em-,-e __ J ' 
Z g 
-g � 1E+O 
�g> E '0. 1E-1 
8sin2(wT/2) 
2nd Order Zero ' , 
1',,' " 
... nl 
o� 
zen 1E-2 
1E-3 
0.01 
, 
.' 
,'16sin4(wT/2) , " 4th Order Zero 
0.1 1 10 
Normalized Frequency 
Fig. 5. Noise Shaping Function. 
100 
Furthermore, assuming a 1/ P phase noise profile of 
SrjJ,llf3 (w), the Noise Power Reduction Ratio (NPRR) 
between the two schemes can be numerically calculated as 
IMS 2010 
2 
NPRR = 
(JI1f1fo,ditt 
(J2 I1f /fO,CDFC 
�T2 Io+
oo 
s '" � (W) sin2 [U�n dw 1fWo "" f3 =--��------�----------�---1 6 f +ocS () . 4 [ wT]d ----Z--T2 Jo '" � w sm 2 w 1fWo "''[3 
= 2(2/(Jlflfo,cDFC "" 9.6dB, (6) 
where calculating (2for a given 1/r phase noise can be 
found in [7]. This shows a noise reduction ratio of up to 9.6dB 
can be achieved by the proposed CDC scheme. 
III. SENSOR SYSTEM IMPLEMENTA nON 
In this section, a CMOS magnetic sensor array with the 
proposed CDC scheme is presented as a design example. 
A. Quad-Core Sensor Cell 
Each sensor cell in the array is designed with four sensing 
sites (four sensing inductors) where any of the four can be 
selected to function as the reference sensor and the other three 
as active sensors (Fig. 6). To maintain correlation of the 1/ r 
phase noise between differential sensing, the four sensing 
inductors are designed to share the same cross-coupled core, 
whose up-converted flicker noise dominates the 1/[3 phase 
noise. NMOS switches are used to couple the desired LC tank 
with the active core. The switches are optimized to minimize 
their 1/ r phase noise which is uncorrelated during 
differential sensing and cannot be suppressed by the CDC 
scheme. The sensing oscillators' nominal frequency is 1 GHz. 
1/1 Noise 
Upconversion from 
Active Devices 
\ 
� 
Sw4 
Fig. 6. Schematic for the quad-core CDFC sensor ceIl. 
B. Sensor System Architecture 
The complete sensor system architecture is shown in Fig.7. 
Sixteen quad-cell sensor cells are implemented on the same 
CMOS chip for high throughput. Multiplexers are designed to 
feed the sensing oscillator output to the on-chip frequency 
counter. Since only one of the sensing sites in each quad-cell 
is needed to act as the reference, each chip has 48 useful 
sensor sites overall. Moreover, this architecture is scalable to a 
large scale array on one chip. In addition, since the sensor 
chip's lOs only have DC supply and digital inputs/outputs, 
multiple chips can be easily tiled for ultra-high throughput 
applications, such as genomic sequencing and genotyping. 
The chip microphotograph is shown in Fig.8, with a zoom­
in view of the quad-core sensor cell. The sensing inductor is a 
978-1-4244-7732-6/10/$26.00 ©201 0 IEEE 618 
l20llm 6-tum symmetric spiral with Q of 6.8 at 1 GHz. The 
sensing inductor spacing of 60llm is chosen to balance the 
integration level with the coupling from adjacent cells. The 
total system power consumption is 80mW. 
••
••
• 
Sensor Quad·Cell #16 " 
,.t;::::::: ::;;::::;:;�==il" 
" , 
....
. 
�s=en='o=.'Q�U=..d�.c�ell�.,�====� 
8:1 
Mu, 
8: 1 f+:!::¢==:J Multtt 
••
•• 
..-.' 
Data elK Latch Start Signal J"""L 
Fig.7. Sensor system architecture. 
\ 
\ 
\ 
\ 
\ 
\ 
\ 
\ 
Zoom-In View of a 
Quad Sensor Cell 
�I U � __ ���M-__ �� � 
Fig.8. Chip microphotograph. 
IV. SENSOR ELECTRICAL PERFORMANCE 
The sensor oscillator's phase noise is measured with an 
RDL phase noise analyzer, achieving -63. 1dBc/Hz and -
135.3dBc/Hz at 1 kHz and 1 MHz offsets, respectively (Fig.9). 
o , 
N :I: -20 
U CD -40 
� 
., -60 III 
� -80 
� -100 .. .r:: 
[l. -120 
-140 
Measured Phase Noise (fo=1.01GHz) 
-Phase Noise (Sensing Site 1) 
-Phase Noise (Sensing Site 2) 
-Phase Noise (Sensing Site 3) 
-Phase Noise (Sensing Site 4) 
1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 1.0E+06 
Offset Frequency (Hz) 
Fig.9. Measured sensor osci\lator phase noise. 
The noise suppression functionality of the proposed CDC 
scheme is verified through frequency counting. The standard 
deviation for relative frequency is measured with respect to 
different counting time T (Fig. 1 0). The correlated noise case is 
sampled from one quad-core cell, while the normal differential 
case is measured using oscillators which are from different but 
spatially adjacent cores to cancel their thermal drift. The slope 
IMS 2010 
of (Yf1flfo (by uncertainty principle) is -20dB/decade, which 
agrees with the analysis in Section II A. 
� 
.;: N 
"':I: 
t:(!) C1 .... 
g� :Jii 
"'� u_ c '" g: � 0.1 
g-[ ... Q. 
u.-:; G.I ;;:: 
�b'l '" 
Qi 
Due to Uncertainty 0l!.flf =12Sppm 
Principle ' 1 ", \ 0l!.f1f,=63ppm 6dB No se '
'
' i-b
' 
� suppri�ion 
----:��-e-i}-- ----3- - -�--{r- -
- � -l( , 0-
___________ :�_,,�--o-----�--�- _ 
�Normal Differential Scheme iJ 
-<>-Correlated Double Counting Scheme 
0:: 0.01 +-_________________ --j 
1.0E-03 1.0E-02 1.0E-01 
Counting Time T (Seconds) 
Fig.IO. Frequency counting Std. with different counting time. 
At large counting intervals (T> O.Ols), the measured 
sensor noise reaches the minimal noise floor set by the 1/ r 
phase noise. The CDC scheme achieves a 6dB noise floor 
suppression compared with the normal differential scheme. 
This value is smaller than the theoretically predicted 9.6dB, 
which is mainly because part of the total 1/ r phase noise is 
uncorre1ated noise from the switch transistors. The frequency 
counting results for different schemes are shown in Fig.II. 
7S0 
N 500 
J: 
=- 250 
:t: 
� 0 
u 
c !!l -250 
Freq. Counting Results (fs•n1• & fRe,) 
b�erential oper1ation 
\ I 
-
, 
3S00 
3000 
2S00 � 
2000 � 
1S00 � 
1000 � 
SOO g! 
cr 
ell 
at -500 +-� -'f'<ill 
o = 
-SOO "i .!:!.. 
-1000 
-1S00 
o 20 40 60 80 100 120 140 160 
Time (Seconds) 
Fig.II. Frequency counting results in time domain for CDC 
scheme, normal differential and no differential operation. 
V. SENSOR MAGNETIC SENSING PERFORMANCE 
To verify the magnetic sensing capability of the sensor, 
DynaBeads® MyOneTM Carboxylic Acid (Diameter=Illm), 
are used as test samples. Solutions with different bead 
concentrations are deposited onto the sensing site, and the 
corresponding frequency shifts are measured. The exact 
quantity of the beads on each site is obtained from scanning 
electron microscope (SEM) images (Fig.I2). 
The normalized sensor frequency shift with respect to 
different bead numbers is shown in Fig.I3. The extrapolated 
frequency shift for one single bead is 342.4Hz with an SNR of 
14.6dB before averaging. The measured sensor response (up 
to 983 beads) indicates an effective dynamic range of at least 
74.5d8. Experiments with bio-samples on genomics level 
(DNA/RNA) and cellular level (bacteria) are currently under 
testing. The results will be reported in the near future. 
978-1-4244-7732-6/10/$26.00 ©20 10 IEEE 619 
Fig.12. SEM image for one sensor site and deposited 
magnetic beads. 
1E6 
af = 342.4 (Hz) x Number � I 
N 1ES of Beads - �
� 
{). e. 0'" � .D-
�� 
U 
� 1 E4 ..0- � � Effective DynaJ,ic 
� 1E3 �� I:I
� Range of 74. Sd1 B 
CDC Scheme Noise 
� 1 E2 f Floor = 63.6Hz ,IJ. 
I::: WFlmFlmF;@� 
1 10 100 1000 
Number of Beads 
Fig. l3. Normalized response for different particle numbers. 
VI. CONCLUSION 
In this paper, a scalable ultrasensitive CMOS magnetic 
sensor array is presented. A noise suppression technique is 
proposed which increases the sensitivity without any power 
overhead. A 64-cell sensor array is implemented in a 65nm 
CMOS process, whose measurements verify both the noise 
suppression capability and the magnetic sensing functionality. 
ACKNOWLEDGEMENT 
The authors would like to thank C-H Lin and Z. Li of the 
California Institute of Technology for their technical support 
on SEM imaging. 
REFERENCES 
[1] S. Han, et aI., "A High-Density Magnetoresistive Biosensor 
Array with Drift-Compensation Mechanism," IEEE ISSCC Dig. 
Tech. Papers, pp.168-169, Feb. 2007. 
[2] Y. Liu, et aI., "CMOS Mini Nuclear Magnetic Resonance 
Systemand its Application for Biomolecular Sensing," IEEE 
ISSCC Dig. Tech. Papers, pp.140-141, Feb. 2008. 
[3] P. Besse, et aI., "Detection of A Single Magnetic Microbead 
Using a Miniaturized Silicon Hall Sensor," Appl. Phys.Letters, 
Vol 80, Issue 22, pp. 4199-4201, Jun. 2002. 
[4] H. Wang, et aI., "A Frequency-Shift CMOS Magnetic Biosensor 
Array with Single-Bead Sensitivity and No External Magnet," 
IEEE ISSCC Dig. Tech. Papers,pp.438-439, Feb. 2009. 
[5] A. Hajimiri, et aI, "Jitter and Phase Noise in Ring Oscillators," 
IEEE J of Solid-State Circuits, vol. 34, no. 6, pp. 790-804, Jun. 
1999. 
[6] O. Akar, et ai, "A Wireless Batch Sealed Absolute Capacitive 
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no. 1, pp. 29-38, Dec. 2001. 
[7] C. Liu, et aI, "Jitter in Oscillators with IIf Noise Sources," 
ISCAS, pp. 773-776, 2004. 
IMS 2010 


An ultrasensitive CMOS magnetic biosensor array with correlated double counting noise suppression

https://authors.library.caltech.edu/23312/1/Wang2010p134182008_Ieee_Mtt-S_International_Microwave_Symposium_Digest_Vols_1-4.pdf

An ultrasensitive CMOS magnetic biosensor array with correlated double counting noise suppression

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