Maximum Achievable Efficiency in Near

Maximum Achievable Efficiency in Near-Field
Coupled Power-Transfer Systems
Meysam Zargham, Student Member, IEEE, and P. Glenn Gulak, Senior Member, IEEE
Abstract—Wireless power transfer is commonly realized by
means of near-field inductive coupling and is critical to many
existing and emerging applications in biomedical engineering.
This paper presents a closed form analytical solution for the
optimum load that achieves the maximum possible power efficiency under arbitrary input impedance conditions based on
the general two-port parameters of the network. The two-port
approach allows one to predict the power transfer efficiency at
any frequency, any type of coil geometry and through any type
of media surrounding the coils. Moreover, the results are applicable to any form of passive power transfer such as provided by
inductive or capacitive coupling. Our results generalize several
well-known special cases. The formulation allows the design of an
optimized wireless power transfer link through biological media
using readily available EM simulation software. The proposed
method effectively decouples the design of the inductive coupling
two-port from the problem of loading and power amplifier design.
Several case studies are provided for typical applications.
Index Terms—CMOS coil, conjugate matching, energy harvesting, inductive coupling, lab-on-chip, matching networks,
medical implant, near-field, neural implant, on-chip receiver,
optimum frequency, optimum load, power transfer efficiency,
RFID, wireless power transfer.
many emerging applications and is commonly realized by means of near-field inductive coupling. This type of
power delivery system is advantageously used for biomedical implants [1]–[3] neural activity monitoring/stimulation
[4]–[7], emerging lab-on-chip (LoC) applications, RFID [8]
and non-contact testing [9]. In this system the circuits contained
in the implant, the LoC or the silicon substrate are remotely
powered by means of a power amplifier operating at a fixed
carrier frequency. Additional functionality is achieved by
modulating the carrier frequency in some manner to realize
unidirectional or bidirectional command and data transfer.
The power efficiency of the near-field link is a measure of: (i)
the power loss in circuits both at the transmitter and receiver,
Manuscript received May 19, 2011; revised July 30, 2011 and October 03,
2011; accepted October 29, 2011. Date of publication January 06, 2012; date of
current version May 22, 2012. This work was supported by NSERC. This paper
was recommended by Associate Editor E. M. Drakakis.
The authors are with the Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S3G4, Canada (e-mail:
[email protected]).
Color versions of one or more of the figures in this paper are available online
Digital Object Identifier 10.1109/TBCAS.2011.2174794
(ii) the absorbed EM energy in tissue1 that causes the local
temperature to increase possibly harming the biological tissue,
and (iii) how often the battery has to be recharged when used
in the context of portable medical devices. Hence in the case
of implants, low-efficiency WPT implementations may cause
discomfort and possible complications for the patients using
it. Similar issues occur in the case of LoC applications where
the local temperature of a small 10 to 100uL biological sample
being measured needs to be held within strict tolerances (often
within one Centigrade degree). Hence it is not possible to arbitrarily increase the strength of the EM fields to realize greater
power transfer to the embedded system.
In most applications, achieving high power-efficiency is extremely challenging due to the restriction on the geometry of the
inductive media. Therefore, a great deal of attention in the literature has been devoted to optimization of near-field inductively
coupled links. Previous authors have addressed the issue of link
optimization using a simple inductor model in air for fixed load
impedance at low frequencies [10]–[18]. Throughout the paper
we refer to an inductively coupled link in air as a simple two-port
model. In most practical applications, the inductive two-port is
designed using numerical electromagnetic simulation software
packages such as HFSS [19] or Momentum [20] that returns S
parameters. Extracting the simple R, L model from these parameters, especially at high frequency, is quite challenging. In addition, and of central concern in this paper, many wireless power
transfer applications require the EM waves to pass through biological material such as skin, muscle, fat, buffer solutions, etc.,
which we refer to as a general two-port model. These media are
conductive and have higher relative permittivity constants than
air [21], [22]. Hence optimizing the link using a simple two-port
model alone and ignoring the media during the optimization
phase incurs large penalties in terms of achievable power efficiency. It is highly desirable for the output voltage to be insensitive to small changes in the distance between the two coils as
well as lateral or angular misalignments. The main focus of this
paper is the efficiency of these links using aligned coils. However the effects of lateral, vertical and angular misalignment specific to our discussions is briefly discussed in Appendix D and
is more generally addressed in [23]–[25].
Recently [26], [27] and others have realized the shortcomings
of the simple two-port model at high frequencies and proposed
the use of S-parameters under simultaneous conjugate matching
to address these issues. However, it is well known that matching
results in maximum power transfer but not necessarily, max1The rate at which energy is absorbed by biological tissue is known as SAR
(Specific Absorption Rate). FCC regulates the acceptable maximum SAR for
RF devices.
1932-4545/$31.00 © 2012 British Crown Copyright
imum efficiency [28]. In fact, conjugate matching has a theoretical upper bound of 50% efficiency while a general two-port can
be designed to have power efficiencies approaching 100%. The
mathematical derivations presented in this paper prove that, unlike conjugate matching, the optimum load is independent of the
source impedance and solely depends on two-port parameters.
Another short-coming in the published classical link optimization techniques is the assumption of fixed load impedance.
This assumption forces an extra unnecessary constraint on the
design of the coupled inductors that could result in sub-optimal
coil parameters. The reported power efficiency in such systems
[10], [14] is between 30 to 50%. By introducing the concept
of optimum load and source impedance one effectively adds
new design parameters to the system beneficially decoupling
the problem of loading effect from the optimization process of
the link. Our proposed approach achieves power efficiencies of
greater than 80% at much greater coil separations to significant
advantage in practical realizations.
An interesting feedback approach was used by [17] to analyze the simple two-inductor model. However their proposed
optimum load and efficiency is an approximation and is not especially accurate at low efficiencies though is increasingly accurate at high efficiency values.
R. Harrison [18] suggested guidelines for maximizing the
power efficiency of a simple two-port case. However no specific optimum load was presented.
Simrad et al. [16] concluded that there exists an optimum load
for which the efficiency is maximized but resorted to numerical
methods to find the optimum load.
Silay et al. [29] studied the effect of loading on maximizing
the power efficiency of the link for a simple two-inductor model.
However they did not decouple the input impedance from the
load and hence their stated maximum achievable power efficiency of 67%, is lower than the theoretically achievable bound.
In addition they all used a simple two-inductor model in air,
which suffers from the same shortcomings stated earlier.
In [14], [30] a four-coil coupled system has been proposed
in an attempt to add a degree of freedom to the effect of load
and source impedance on the power efficiency of the system.
However any method of impedance transformation introduces
additional losses due to the finite quality factor of the components. In the case of four-coil systems, the transformation is carried out using coils with Q values up to 150. As we will see
in Section III, the proposed method of matching networks uses
discrete capacitors and inductors. The capacitors have Q values
higher than 1000. Therefore the matching networks using only
capacitors tend to have lower penalties in terms of efficiency.
In addition to this, having four coupled coils increases the cost,
size, complexity of design and enforces several constraints on
the inductor geometry.
This paper presents the first published result that optimizes
the near-field link based on the general two-port parameters of
the network. In this approach we introduce the concept of optimum load for any passive two-port network. We also derive
a simple closed-form expression for the maximum achievable
power efficiency of the given two-port and show that it is theoretically possible to approach 100% power transfer efficiency.
These results also provide insight into the design of such links
Fig. 1. General two-port power transfer system model.
by introducing a simple criterion on the two-port parameters to
maximize power transfer efficiency. Moreover, the results are
applicable to any form of passive power transfer such as inductive or capacitive coupling. These derivations provide a powerful tool for modifying the simple two-port inductor model to
the more complicated but realistic general form (e.g. adding the
conductance between the two coils to model the conductivity of
media) and quickly observing the effects on the efficiency and
optimum loading in the system. Therefore, it is easy to optimize
a realistic wireless power transfer link through biological media
using readily available EM simulation software.
The optimum load is realized using matching networks. However, these matching networks are usually lossy and affect the
maximum achievable power efficiency. In this paper, we address
these issues and comment on the design of the matching stages
to achieve optimum efficiency. The remainder of the paper is
organized as follows. In Section II, we introduce simple optimization criteria to achieve maximum power transfer efficiency
through a general passive two-port network. Using these criteria we introduce the maximum achievable power efficiency
and optimum loading condition for a general passive two port.
In Section III, we discuss how to mitigate loss of efficiency in
matching networks and the resulting optimum number of stages
for matching. Section IV, provides several case studies on inductive coupling through air, biological tissue encountered in
implants and blood for lab-on-chip applications. Throughout we
make quantitative comparisons with measured published results
whenever possible.
In this section we will derive the power transfer efficiency, or
simply power efficiency, of a general passive two-port network
from the source to the load. Fig. 1 shows the block diagram for
a general inductive-coupled power transfer system. The power
efficiency, or simply the efficiency, of the system is defined as
is the power delivered to the load and
is the power
delivered by the source
. The value of depends on var, the source impedance
ious parameters such as the load
, the impedance loading the source
and the two-port
parameters. Therefore to achieve the maximum possible efficiency in the system we need to be able to freely choose the load
and the desired input loading
. As shown in Fig. 1,
these impedance conversions are realized using the matching
networks. In order to obtain the maximum possible efficiency
of a two-port we first derive the efficiency for Fig. 1, then we
that would result in the
introduce the conditions on
maximum possible value for (1). A general linear two-port is
represented in terms of its ABCD parameters
. The value of
efficiency in (10) is given by
that allows for the maximum
Without loss of generality we choose the desired impedance,
, to be n times smaller than
where n is an arbitrary positive real number. Therefore, the
voltage at the input of the two-port due to the source is
is the impedance at the input of the two-port and
denote the real and the imaginary parts of the
expression. In (5) we have assumed that the matching networks
are lossless. This assumption is revisited in detail in Section III.
is then transformed by the two-port gain and
The voltage
shows up at the second port as
As seen from (12) and (13), in general, the proposed optimum
load is not matched to the two-port. Therefore, the optimum
power efficiency does not happen when the load is matched to
the two-port. In fact matching would never result in efficiencies
higher than 50% while (10) can theoretically be as high as 100%.
in (10) is a function of
and represents
The term
the efficiency from the source to the input of the two-port for a
depends on the input
linear voltage source. The choice for
driver . In practice, the two-port is driven by a class-E power
should be replaced by the effiamplifier. Therefore,
ciency of the employed power amplifier. Thus a more realistic
form of (10) is given by
We can then simplify the expression by substituting
its ABCD parameters
Hence (6) simplifies to
Using (8) the power efficiency from
is the admittance of the
lated, where
can be calcu-
As expected, is a function of . Hence, there exists an opthat would maximize . Therefore by maxtimum load
imizing (9) with respect to the real and imaginary parts of the
and replacing the ABCD parameters with Z-paload
rameters, we can show that the maximum achievable efficiency
under optimum loading conditions in any passive two-port network is
represents the power amplifier efficiency and
is the two-port efficiency. The efficiency of a power amplifier
is a function of its load and this is what drives the choice behind
. It is a well-known fact that there exists an optimum
, which maximizes the power
load, usually referred to as
delivery efficiency of a power amplifier. The value of
completely different from the small-signal output impedance of
the power amplifier and is generally found using load-pull techniques [28]. Therefore, to maximize the power from the source
to the load it is essential that the two-port would provide the aploading for the power amplifier. The
is theoretically
efficiency of a class-E power amplifier
100% and in practice efficiencies higher than 75% are achievable. The second term in (10) is a function of two-port parameters. In order to maximize the power efficiency of the two-port,
we need to maximize . Fig. 2 shows the maximum possible
power efficiency from the two-port to the load as a function of
the variable .
Equations (12) and (13) represent the optimum series load.
The equivalent parallel load is calculated in (16), (17). These
quantities are best represented in terms of the network Y
Fig. 3. Simple model for inductive power transfer.
It is no surprise that (22) exactly matches with the maximum
power efficiency derived in [10] for the same simple circuit. In
order to gain some understanding about the optimum load, we
will further simplify the model and assume that the capacitances
are cancelled out by the matching network, the optimum load for
the network in this case is
Fig. 2. Maximum two-port efficiency as function of .
Appendix F studies the variations in power transfer efficiency
as the load deviates from the optimum load. It is interesting to
note that the well-established simultaneous conjugate matching,
that is widely used in microwave amplifiers [31] and guarantees maximum power transfer, occurs in the special case where
. In this situation the
, where
is the maximum available power from
the source, which is realized under source matching condition
. In the literature
is commonly referred to as
happens under
transducer gain. The maximum value for
the simultaneous conjugate matching condition and it is usually
stated in terms of S-parameters [31]. For a passive two-port
By simplifying (18) in terms of the two-port parameters it can
be shown that
Therefore, our result agrees with the well-established
has 50% as its
under the matched source condition.
upper bound for the efficiency. In order to provide more insight
into what each of these quantities represent we can relate them
to a first-order, simple inductive coupling two-port model. Fig. 3
shows the circuit block diagram for such a two-port system.
Using Fig. 3 we find that
are the quality factor for each of the inductors
and k is the coupling factor between the two coils. This shows
that for a simplified model, in order to increase the efficiency
we have to increase the mutual inductance and minimize the resistance. Using (15) the maximum achievable power efficiency
from the two-port to the load is given by
is the reflected
from the source
side. As is evident in (24), the imaginary part of the optimum
load would completely ignore the impedances transferred from
the input side and only resonate out the imaginary part of the
coil, hence maximizing the voltage on the load. Once again (24)
perfectly matches the common practice of resonating out the
load presented in [10]. Unfortunately, (24) only holds as long
as the coupling is purely reactive. A good approximation of this
case is when the two inductors are coupled through air, similar
to this simple example. The picture changes, when the media
in between the coils is conductive,
, e.g tissue or
biological media. In such scenarios the optimum load should be
calculated using (12) and (13). The optimum load impedance
balances the current between the conductive and inductive path
such that the efficiency is maximized. Hence the resulting load
is different from what is commonly practiced (resonant tuning)
in the design of implantable wireless power delivery systems.
In the derivations presented up to this point, the losses of the
matching networks were neglected. In Section III, we consider
the effect of these non-idealities on the total efficiency of the
The conversion of load impedance to the optimum load
and the input impedance
to the desired impedance
has to be conducted through a filter commonly referred to
as a matching network. Matching networks can transform any
impedance with non-zero resistance to any desired resistance.
The reactive part of the desired load is then easily adjusted by
adding a reactive component in series or parallel. Therefore
without loss of generality we will assume that the matching
network is transforming a general complex load to a purely
resistive desired load. There are different types of matching
networks such as , T or L to choose from [31]. In situations
where the quality factor of the matching network is not enforced and efficiency is of primary concern, L-match is a good
choice [28]. Therefore in this section, the analysis are based
on multi-section L-match networks. This being said, similar
derivations can easily be developed for other types of matching
Using (32), (33) the loss in the matching network is found to be
Fig. 4. L-match sections for two different conversion cases (a) and (b).
networks. L-match networks are aptly named as they consist of
two elements that form an L-shape circuit.
A. L-Matching Network Analysis
Depending on whether we need to increase or decrease the
real part of the impedance, one of the two L-Section circuits in
Fig. 4 is used.
The efficiency loss through the matching network is due to the
resistance of components used in the matching network. In the
following analysis we assume the losses are small enough not
to affect the impedance conversion operation of the network.
. The Q of the
We will first address case (a) where
L-match network is defined as
On the load side, usually we need to step down the load (com. On the source side, on the other
monly case b) and
hand, the load resistance is the series resistance of the trans, and theremitter coil and is small. We will refer to this as
fore we are commonly dealing with case (a). Equation (21) suggests that high efficiency occurs when the coils have high Q.
. In such scenarios the loss through the
matching networks can be simplified to
Using (25), the value of the reactance X and susceptance B
are given by
Using (26), (27), the portion of power loss due to
is calculated to be
Therefore, it is vital to use very high Q components. The
transmitter inductors made using PCB traces have a Q between
50 and 250 in air, therefore, the series matching component on
the source side needs to be a capacitor with a very high Q. On
the load side however we can improve the efficiency by reducing
the effective Q.
B. Optimum number of stages
According to (25), (31) the Q of the matching network using
one stage may become large which as we saw in (30), (36) can
hurt the power efficiency of the conversion. A remedy can be
found by using multiple stages, each stage having . Using (38)
the efficiency of each section i is
are the Q of the
series and parallel components used in the matching network
is quality factor of the load. Assuming
, the total efficiency through the matching
network for case (a) is found to be
We can follow the same procedure for case (b) where
The quality factor in this case is given by
There exist an optimum number of stages that maximizes the
total efficiency
Using the proof presented in Appendix C, all stages should provide equal impedance conversion and the optimum number of
stages for large Q is
The value of reactance X and the susceptance B for case (b) are
given by
A natural question at this point is, what is a practical achievable value for and how much does the efficiency degrade with
a non-optimal load. How does biological tissue or relevant liquids such as blood affect the optimal coil design strategy? Is it
possible to integrate the receiver coil on-chip using a standard
CMOS process? Is there an optimum frequency of operation to
maximize power transfer efficiency? In order to address these
questions, we present three different case studies to demonstrate
the power of the derived equations, verify the derivations in
Section II, and provide intuition on possible achievable power
efficiencies in different media as well as insight into coil design.
It will become evident through the examples that the established
conventional wisdom regarding coil design for biological tissue
needs to be revisited.
A. Case Study 1: Two Coil Power Transfer through Air and
Muscle Media
Question: How does a conductive medium with higher relative permittivity between two coils affect WPT design? It is
generally understood that losses through biological tissues and
liquids are negligible at frequencies between several hundred
kiloHertz to 20 MHz. As a result designers often ignore the effect of biological tissue during the coil design process [11], [12],
[14], [32], [33] and optimize the coils assuming air as the media
between the two coils. In this example we challenge this conventional wisdom and re-evaluate coil design for conductive media
such as muscle. In order to highlight the effect of biological
tissue in between the two coils and to validate our simulation
results we use the measurement data from [11], [34].
Jow et al. [11] proposed a coil design strategy for powering
up biological implants. The paper presents measurement results
for air and claims that the design when used in the context of a
biological implant would not significantly affect the efficiency
under 20 MHz. In order to further investigate this claim and
answer the question of whether or not the coil design has to be
revisited, we first use Momentum [20] along with our method
to reconstruct the measured data from [11] for the case of a
simple two-port with air between the two coils. Then we use
the same set of coils for the case of a general two-port to power
up an implant buried under layers of skin, muscle and fat. The
experiment [11] uses two 1-oz copper FR4 PCB pancake coils
with 10 mm of separation. They report a measured efficiency of
75% at 5 MHz from the input of the two-port to the load. The
summary of the coil parameters is shown in Table I.
Fig. 5 shows how these coil parameters map to the specific
geometry of the implemented coils. We used Momentum [20]
to simulate the coils. We used (9) to consider the effect of load
impedance on the efficiency. The real part of the load impedance
was set to 500 [11] and the imaginary part was set to cancel
from the two-port.
Fig. 6 shows our simulated data versus the measurement results from [11]. There is very good agreement between our simulation results (b) and the measured data (a) presented in [11].
Next, we replace the air media in our simulations with 1 mm
of skin, 2 mm of fat and 7 mm of muscle media as shown in
Fig. 7. The frequency dependent permittivity and conductivity
of the tissue can be modelled using the four parameter cole-cole
model presented in [22]. The summary of dielectric properties
at 5 MHz is presented in Table I. In order to obtain the maximum
possible efficiency, we used the optimum load (12), (13) for
Fig. 5. Geometry of the coil based on d , w and s.
Fig. 6. (a) Measurement results from [11] for Coils A1/A2 with 10 mm of Air
separation and 500 load. (b) Momentum simulation using (9) for Coils A1/A2
with 10 mm of air separation and 500 load. (c) Simulation results for Coils
A1/A2 with 10 mm of (skin+fat+muscle) separation and optimum load (12),
(13). (d) Simulation results for Coils B1/B2 with 10 mm of (skin+fat+muscle)
separation and optimum load.
the simulations with biological tissue as the medium. Fig. 6 depicts the maximum achievable efficiency using the coils A1/A2
from [11] in the presence of tissue. The results indicate that the
maximum achievable efficiency drops to 1% at 5 MHz. However, the huge loss can easily be overcome by redesigning the
coils. Our simulations show, contrary to intuition, and unlike
coupling through air, a greater number of turns would strongly
degrade the efficiency. Hence coils designed for power transmission through conductive biological tissue have only a few
turns. In order to demonstrate this point, a new set of coils B1/B2
Fig. 7. 3D perspective of the biological media between the two coils.
were designed for the new tissue environment. The coils were
designed under the same constraint of 40 mm and 20 mm outer
diameters. The geometry of the new proposed coil design can
be found in Table I. Notice the large reduction in the number of
turns. As shown in Fig. 6 the efficiency of the proposed coils
is 77%. In order to find the effect of tissue on the maximum
power transfer efficiency, we need to compare the results with
an air optimized set of coils under the same constraints. Our
simulations shows that the maximum power transfer efficiency
for such coils is 83.8%, which is only 6.8% higher than the case
when tissue exists between the two coils.
The results indicate that the losses through biological sample
media can indeed be made negligible at low frequencies. However the design of the coils needs to be revisited. The presence
of muscle has a major impact on the self-resonance-frequency
(SRF) of the coils. The measured SRF in air of coils A1 and A2
are 28.2 MHz and 24.5 MHz [11], respectively. We simulated
the new value for the SRF when the coils are surrounded by
muscle and the SRF was degraded to 5.5 MHz and 12.5 MHz,
respectively. Therefore by increasing the SRF in coils B1, B2
the power efficiency is restored. Coils B1, B2 have a SRF of
63.3 MHz and 125 MHz, respectively.
The authors in [11] revisited their approach for tissue in [34]
and proposed a new set of coils with higher SRF for powering
up an implant buried under 10 mm of muscle. The experiment
used two coils fabricated on separate 1-oz FR4 substrates. The
coils had 10 mm of separation. The gap between the two coils
was filled with medical grade silicone, muscle and plastic bags
and achieved an efficiency of 31%. The authors propose using
medical grade silicon on top of the coils to mitigate the lowering of SRF due to the muscle. In the remainder of this case
study, we first simulate the measurement results from [34] and
then comment on how we can further improve the efficiency.
Table II shows the media used for simulating the measurement
results from [34] and Table III shows the geometry for the coils
as well as the properties of the substrate. The data in Table III
were extracted from [34]. In order to reconstruct the measurement results in [34], we simulated the coils C1/C2 using Momentum. The measured power efficiencies represent the power
efficiency from the two-port to the 500 load. Table IV presents
a summary of the results.
Our simulation shows that under optimum loading condition,
using a fewer number of turns and larger spacing between the
traces, efficiencies up to 73% are achievable even without the
The stated efficiency includes the losses due to matching network.
Without medical graded silicon coating on top of the metal traces.
presence of the expensive medical grade silicon coating. However, the parallel optimum loading for the new coil geometry
is close to 11 , which is much smaller than the nominal load,
500 . The 11 resister value was calculated by substituting the
parameters from Table III into (23) and (24) and converting the
the calculated series impedance to parallel. Therefore matching
networks are essential for harvesting the higher efficiency. It is
evident that adding matching networks introduces an extra degree of freedom in the design, which can be exploited to our
advantage. In fact using the coils D1/D2 under the traditional
resonant tuning condition and 500 load would reduce the efficiency down to 12%. Fig. 8 shows the achievable efficiency as
a function of frequency for both of these scenarios.
The load impedance is set to 500 and we compare resonant
tuning versus optimal load using matching networks. In conclusion, we observe that the losses through tissue can indeed be
Fig. 8. Power efficiency from the two-port to the load through muscle using the
set of coils D1/D2. (a) Using matching networks for optimum load at 13.6 MHz.
(b) Using resonant tuning for the 500 load.
made negligible at low frequencies, however the media has to
be considered during the design procedure. Unlike the coils optimized for air media, coils optimized for the general two-port
model tend to have a very few number of turns (usually under 3)
and larger spacing (s) in between the traces. As a result the design space of optimum coil design is quite small compared to the
case for air alone. Therefore, because of the reduced parametric
design space for the coils the design can quickly be optimized in
a few iterations using an EM simulator and (15). The optimization process only needs to consider (15) and can ignore the load,
as the optimum load can always be realized using matching networks with only a few percent penalty in efficiency.
Fig. 9. The measurement setup. (a) Micrograph of the on-chip coil and CMOS
core circuitry. (b) Details of CMOS structures used in the simulation model.
(c) Overall geometry of FR4 Tx and CMOS Rx coils as specified in Table V.
B. Case Study 2: Receiver Coil on CMOS Silicon Substrate.
Question: Can a WPT receiver coil, integrated on a lossy
CMOS silicon substrate, be designed with high power transfer
efficiency? If so, what circuit design insights need to be followed? Integrating the receiver coil using a standard CMOS fabrication process would significantly reduce the total system cost,
especially in the case of embedded implant and lab-on-chip applications. In this example we explore the possibility of having
the receiver coil integrated on a CMOS silicon substrate while
the transmitter coil is realized of copper on FR4 substrate. By
fully integrating the receiver coil with an on-chip matching network the conventional chip package and requisite encapsulation
can be eliminated.
Inductive coupling works on the basis of Faraday’s law of
induction, and as such is therefore, to first order, proportional
to the area of the receiver coil. Hence it is immediately evident
that by integrating the receiver coil the expected efficiency will
suffer. In addition to this, the silicon substrate has higher loss
associated with it compared to an FR4 substrate and the CMOS
metal layers are thinner and hence more resistive compared to
1-oz copper traces. Therefore, is it possible to practically realize
such systems at high transfer power efficiency using on-chip
coils? What is the optimum frequency of operation? What is the
optimum geometry for the coils? Can we integrate the matching
networks on-chip? The following case study has been designed
to answer these questions in the two sub-sections that follow.
1) WPT to a CMOS Receiver Coil through Air: In order to
demonstrate the validity of our simulation results in the presence
coil in top-layer
of a CMOS substrate we fabricated a 1
TSMC CMOS process. The area in
metal in a standard 0.18
the middle of the coil was occupied by 0.95
of active and
passive CMOS circuitry. The CMOS integrated coil was then
powered up by a PCB board held 10 mm above the CMOS integrated coil with air as the media between the two coils. Table V
shows the details of each coil.
The power transfer efficiency was measured using a Verigy
(Agilent) 93000 SOC tester. This test environment dictated that
the nominal load during the measurement was 50 . We used
matching networks to convert the 50 load to the optimum load
(126.1–195.58i) for the on-chip coil. On the source (PCB) side
to be the same as the source
we set the desired impedance
impedance of the tester 50 . The value of the optimum load and
the input impedance were calculated using (12), (13). The simulations were performed through Momentum. We simulated the
TSMC CMOS parameters
full CMOS substrate using 0.18
as well as the top three metal layers and the results showed an
excellent match between simulations and measurement. Fig. 9
shows the geometrical setup of the simulation as well as the die
photo and Fig. 10 shows the measurement results versus the simulations obtained from Momentum. The measured efficiency
shows the ratio of the power delivered to the 50 load in the
Fig. 11. (a) Two parallel coils. (b) Two series coils manufactured using the top
two metal layers.
Fig. 10. Simulation versus measurement results for Case Study 2 part B.1.
presence of the matching networks versus the power delivered to
the two-port. The coils were not optimized for the distance separation or frequency chosen, but nevertheless, it illustrates the
predictive nature and the ease of use for the method proposed in
this paper even in the presence of a lossy CMOS substrate.
2) WPT to a CMOS Receiver Coil through Tissue: In this part
we first recreate the scenario presented in [26], [35], [36]. The
authors state that the optimum frequency for powering a typical
set of miniature coils, mm-sized, through tissue is in the GHz
range, and that this frequency drops to a couple of hundred MHz
for the case where one of the coils is larger (cm-sized). They
off-chip receiver coil
present measurement results for a 4
transmitter coil. The power is transferred through
and a 4
air and 15 mm of muscle. Their measurement results achieve
a total power efficiency of 28.4 dB at 915 MHz. Though the
exact geometry of the coils and their substrate are not presented
in the paper, we infer a set of parameters where our simulation
results are very close to the measurement results from [36]. In
order to recreate the measurement results we assumed an FR4
substrate for both the receiver and transmitter. The simulation
setup uses a receiver coil adjacent to 15 mm of muscle [22] as
well as 10 mm of spacing of air between the transmitter and the
muscle. Table VI summarizes the geometry, the substrate, the
simulation and the measurement results.
The achieved power efficiency of 28 dB is acceptable for
some biomedical applications. However, in this example both
coils were fabricated on an FR4 substrate. It is natural to ask
how the result would change if the receiver coil is manufactured
on-chip in a standard CMOS process. Hence, in order to answer
this question, we simulated an on-chip receiver coil fabricated
IBM CMOS process. The simulation modeled the
in a 0.13
IBM CMOS substrate with 13 different layers of
full 0.13
dielectric. In order to make a fair comparison between the two
and the
cases, we limited the size of the receiver coil to 4
maximum metal width was enforced by the design rules of the
. The coil inductance was increased
CMOS process to be 140
using two different metal layers in series. Fig. 11 demonstrates
the series on-chip coil. Using this new setup the maximum possible efficiency we could achieve at 915 MHz was 46.055 dB,
which is too small for practical applications. The huge loss in
power efficiency is due to the losses through the silicon substrate. The substrate also reduces the SRF of the receiver coil.
Therefore it is obvious that lower frequencies are more suitable
for on-chip power receivers and 915 MHz is not the optimum
frequency of operation for this case. In a search for the optimum
frequency for on-chip coils, we simulated a wide range of frequencies from 40 MHz to 950 MHz and the maximum power
efficiency of 33.1 dB occurred around 115 MHz. As is evident the loss increased due to the silicon substrate at 915 MHz
and thus reduced the power efficiency by 18 dB. However, by
lowering the frequency to 115 MHz we can recoup most of these
losses. Even in the scenario where the design is restricted to an
ISM band our simulations shows that a frequency of 40.68 MHz
results in 34.75 dB which is still 11.3 dB higher than the
achieved power transfer efficiency at 915 MHz. Further improvement in the power transfer is realized by reducing the size
of the transmitter as well as the 10 mm gap between the transmitter and the muscle. The resistance of the coils was reduced
using two different metal layers in parallel as shown in Fig. 11.
The optimum frequency for new setup is now 120 MHz yielding
a power transfer efficiency of 26.17 dB. Table VII specifies the
geometries used for this simulation.
Therefore, to conclude it is possible to integrate the receiver in
a standard CMOS process. However, in the presence of biological tissue the optimum frequency is approximately 100 MHz
and not in the GHz range. Finally in order to make a comparison between optimum loading condition presented in this
paper and the traditional conjugate matching, we have plotted
the power transfer efficiency from a source with 50 impedance
load in Fig. 12. The maximum possible theoretical
to a 1.4
efficiency curve (c) represents (10) with zero source impedance
for frequencies between 115 MHz to 125 MHz. The optimum
The CMOS coil consists of two turns of top metal in parallel
with two turns of second top metal layer.
The CMOS coil consists of two turns of top metal in series with two
turns of second top metal layer.
Fig. 12. Power transfer efficiency through muscle from source to the load using
coil set F1/F2 (a) under optimum loading conditions R = 500
and matching
networks tuned to 120 MHz, (b) under simultaneous conjugate matching tuned
to 120 MHz, (c) maximum possible theoretical efficiency R = 0.
loading condition (a) shows the same two-port including the
matching networks tuned to 120 MHz and with the desired re, set to 500 . Finally the simultaneous conjugate
matching (b) represents the power efficiency in the presence of
matching networks tuned to 120 MHz.
As is evident the optimum loading conditions results in higher
efficiency compared to simultaneous conjugate matching.
C. Case Study 3: WPT to Fully-Integrated CMOS Receiver
Coil and On-Chip Matching Network Immersed in Blood
Question: What circuit design compromises are needed to
fully integrate in CMOS the on-chip matching network with the
receiver coil? In Case Study 2 part B.2 we saw that it is possible to integrate the receiver coil and reduce the cost of fabricating a miniature 4
coil with a metal width and spacing
on the order of 10’s of micrometers. However the matching networks at 120 MHz involve component values that are too large
to be implemented on-chip. In this case study, we consider the
possibility of a fully integrated lab-on-chip receiver immersed
in blood capable of delivering 1 mW of power to a 1.4
load at 1.2 V supply using a 150 mW transmitter. Table VIII
specifies the properties of the transmitter and receiver along
with the media. In order to maximize the efficiency we started
with a 1-turn transmitter coil G1 on Rogers RT/duriod 5880 and
two-turn top-layer metal coil G2 on a CMOS substrate. Using
(15) we searched for the optimum frequency of operation between 80 MHz to 150 MHz. The maximum efficiency occurred
at 140 MHz. However the optimum load (12), (13) at this frequency required a capacitance of 126 pF, which is too large to
be implemented on-chip. Also the DC resistance of the optimum
load was determined to be only 280 . Converting the 1.4
load resistance to 280 requires an on-chip matching network
with inductors that occupy large area and have very low Q and
hence are not suitable for our design.
One possible solution to this problem is to increase the frequency of operation. This would increase the DC resistance of
the optimum load and reduce its capacitance but at the cost of
lower efficiency. At 400 MHz the load capacitance is reduced
to 16 pF and the optimum load is 800 at the cost of 5 dB
loss in power efficiency. Using (12), (13) we can see that another way to address the issue of the optimum load problem
is by increasing the inductance and resistance of the receiver
coil using more metal layers in series for the receiver coil as
shown in Fig. 11. Using this new receiver coil configuration we
were able to increase the DC resistance and reduce the capacitance without increasing the frequency much higher than the optimum. Table VIII shows the final choice for frequency as well
as the geometry of the coils that provides an attractive compromise in design parameters. At 180 MHz the efficiency is 0.5 dB
lower than the optimum frequency but now the DC part of the
optimum load matches our desired load impedance and the required capacitance for the optimum load is 15.1 pF, which can
easily be implemented on-chip.
Generally speaking, optimum planar coils with on-chip
receivers can easily be designed for power transfer through
biological tissues by following a few simple guidelines: 1) The
optimum frequency of operation is around 100 MHz with
40.68 MHz as the closest ISM band. 2) The outer radius of
the transmitter, in the case of circular loops, should satisfy
is the distance between the coils [37].
This constraint is modified to
for the case of square coils (see Appendix E). 3) The optimum
. 4) The trace
transmitter has only a few turns, typically
to achieve
width for the transmitter is generally
high Q. 5) The outer dimension of the receiver coil (on a silicon die) should be the largest value permitted by the die area.
6) In CMOS processes with DRC rules that constraint maximum
wire width, use two or three top metal layers in series for the receiver coil with maximum allowed wire width. 7) The last step
is to optimize (15) by sweeping the trace width, spacing and
the number of turns in an EM simulator. Usually, this process
quickly converges due to the constrained design space. 8) Once
the optimum geometry has been found, the optimum load and
desired loading for the power amplifier can be independently realized using matching networks.
The case studies presented in this section use planar structures. Such structures are becoming more popular due to the
low fabrication cost and more flexible geometry. However some
biomedical circuits use non-planar coils. These helical coils can
easily be simulated using 3D EM simulators such as HFSS [19].
HFSS also produces S parameters which can easily be used to
calculate the optimum load and predict the maximum achievable
efficiency. Helical solenoids tend to have lower self-resonance
frequency compared to planar coils and hence are usually operated in the kHz to low MHz range [12], [38].
In this paper we have studied inductive power transfer
through a media in its most general form using two-port
parameters. The two-port approach makes no simplifying
assumption about the type of media or the characteristics of
the coils. Therefore it is capable of correctly predicting the
power transfer efficiency at any frequency, through CMOS
substrate or biological media. We presented a closed form
analytical solution for the optimum load that would maximize
the efficiency of power transfer. Using this optimum load we
have found the closed form solution for the maximum possible
power efficiency under arbitrary input impedance conditions.
The concept of optimum load decouples the design of the coils
from the load. Therefore the coils can be optimized independent
of the load while fully considering the media surrounding the
coils. However realizing the optimum load requires matching
networks which tend to be lossy. We introduced simple equations that can predict the efficiency loss due to the matching
networks as well as the optimum number of matching stages
for achieving minimum efficiency loss.
Finally, we introduce measurement and simulation results for
several case studies such as power transfer through air, muscle
and blood using coils integrated on FR4 and CMOS substrates.
The case studies demonstrate the insight provided by the optimum load condition as well as the ease of design using the
equations derived in Section II. The results show that optimum
coils for biological and lab on chip applications tend to have
only two or fewer number of turns and hence can be quickly optimized due to the constrained design space. We also showed
that it is possible to fully integrate the receiver coil and the
appropriate matching network on a standard CMOS process
without a significant loss in power transfer efficiency relative
to the predicted optimal value.
Assuming that we have lossless matching networks, the input
power to the matching network should be equal to the output
power hence
Using (4) we can simplify (43) to
Finally, a simple algebraic manipulation produces
In order to calculate
we need to take the partial
, and
derivative of (9) with respect to the real,
, parts of
. We will ignore the
term in (9) during the optimization process. The first
step is to simplify (9) in terms of ABCD parameters
where rA, rB, rC, rD represent the real part and
represent the imaginary part of the ABCD parameters. Next we
take the partial derivatives of (46) ignoring the
Next we need to simultaneously set the two partial derivatives
to zero
The solution to (52), (53) is shown as follows:
Converting (54) and (55) from ABCD to Z-parameters results
The optimum number of stages, N, is given by
The overall efficiency that we are trying to maximize, using
N stages is given by
where k is a function of
0.05 for
and is approximately 2
, therefore for large Q values
The matching network has to realize a total impedance converhence the following constraint exists on the
sion ratio of
impedance conversion of the subsections:
Using Lagrange multipliers method, we can maximize the following equation:
Using simple algebraic manipulations we find that the maximum of the function occurs when
Now assuming
A. Example for Optimum Number of Stages in Fig. 4
In this example a 5 load is being up-converted to 442
using structure (a) in Fig. 4 and a 2
load is being down-converted to 10 using the second configuration. Table IX summarizes problem for different numbers of matching stages. As you
can see the equation for the optimum number of stages (64) provides the highest power efficiency for the matching networks.
The derivations in Section III assumed that the parasitic resistance of the components has no impact on the impedance
conversion. However these unwanted resistances can be comor the desired resistance
. Hence the
parable to the load
would deviate from the desired
actual input impedance,
. Table IX shows that the mismatch improves
with larger number of stages.
Fig. 14. B component of the magnetic field of a wire carrying current I at
point (x; y; z ).
Fig. 13. B component of the magnetic field of a bent wire carrying current I
at point (x; y; z ).
I. The
component of the magnetic field at an arbitrary point
in the space is given by
Using (9) we can approximate the change in the efficiency
due to misalignments, change in the distance between the coils
or tilting. The following derivations assume that the structure
is using the optimum load for the no misalignment case. These
derivations are based on the simple two inductor model shown
in Fig. 3. Any misalignment would result in change (usually reduction) in the mutual inductance between the coils. However
at low frequencies the other two-port parameters tend to stay
constant. Hence by taking advantage of this fact and in order
to capture the deviation from the optimum power efficiency we
derive the Taylor series for (9) in terms of Z parameters with re. We can use the following transformations
spect to
from ABCD parameters to circuit parameters for the simple case
shown in Fig. 3:
Now using (67) we can derive the equation for the current loop
in Fig. 14.
Hence the Taylor series is given by
The next step is to find
as a function of geometry
and misalignment. In this appendix we present the case where
both coils have square spiral shape. Similar derivations can be
performed on circular structures. Calculating the mutual inductance requires knowledge of the magnetic field generated by one
of the coils at each point in the space. Without loss of generality,
we will assume that the coils are in the
plane. Hence we need
component of the field. A square loop consists of four
wires. Fig. 14 shows such a square loop carrying current I. The
component of magnetic field,
, generated by such a loop
at an arbitrary point
can be found using Biot-Savart’s
law. In order to find the field we can break the loop into two
segments and derive the magnetic field for each portion. Fig. 13
shows two wire segments of length 2W and 2K, carrying current
Fig. 15. Efficiency as a function of lateral misalignment using Momentum
(ADS) and MATLAB (66). A misalignment of 15 mm corresponds to the right
outer edge of coil H2 being aligned with the right outer edge of coil H1.
is the length of the edge of the square loop as shown
in Fig. 14. In practical cases where the wire traces have finite
. For the genwidth,
eral case where the coil has turns, each turn can be treated as
an individual loop carrying current I. Hence the generated magnetic field at each point in space is the superposition of the fields
due to each individual turn
The mutual inductance between two spiral coils
and with
turns, respectively, can now be easily calculated
using (68), (69)
represents integration over the area of each
and every loop at the receiver. This integral can easily be evaluated numerically in MATLAB. In order to show the utility of
(66) we have simulated the efficiency of two square spiral coils
as a function of lateral misalignment between the two coils. The
simulations were performed in ADS (Momentum). The properties of the coils are presented in Table X. The assumed load
during the efficiency simulations is the optimum load for no misalignment case. Therefore, we do not update the load to the optimum load as we introduce misalignment to the structure.
As is obvious from Fig. 5 these coils are not fully symmetric
with respect to and axis. Therefore the efficiency simulations
presented in Fig. 15 for each misalignment value is the average
of the power efficiency when the center of the smaller coil is
moved up, down, right and left with respect to the center of
larger coil. The calculated values of mutual inductance using
(70) had up to 25% error with respect to the simulation results
from ADS however the maximum error in
was only
8%. Fig. 15 illustrates the results.
Fig. 15 shows that the efficiency drops by 45% when the right
edge of the smaller coil is aligned with the right outer edge of
transmitter coils (15 mm misalignment). A similar simulation
was done for Case Study 3 and the efficiency was reduced by
40% under the same condition. In addition to this, the loss in efficiency due to deviation from the optimum load even at the extreme case of 15 mm lateral misalignment or 10 mm of vertical
misalignment was less than 1%. The exact same steps could be
applied to the vertical misalignment scenario. However in situations where we are only dealing with vertical misalignment, we
can simplify (70) by assuming that the magnetic field produced
at the receiver coil is uniform and equals the field at the center of
the coil. The mutual inductance between a square spiral transturns and a receiver coil with
turns can
mitter coil with
be approximated using the following equation:
where represents the effective area of the receiver coil. This
area can be calculated using the following formula:
is the pitch between two consecutive turns and is half of the outer diameter
of the receiver
coil. Equation (71) over-estimates the mutual inductance. Nevertheless, the simulation results are in a good agreement with
calculations from (71). Fig. 16 shows the simulation versus calculation results for vertical misalignment.
The last case addressed in this section is angular misalignment. We assume that the normal vector of the receiver coil is
tilted degrees with respect to the Z axis in Fig. 14. As a result
the effective area in (72) should be updated to
where for small values of
this can be approximated by
Fig. 17. Loss in efficiency due to deviation of B from B
Fig. 16. Efficiency as a function of vertical misalignment using Momentum
(ADS) and MATLAB (66) where coils H1 and H2 are normally separated by a
distance of 10 mm.
Since we interested in the percentage of change from the maximum achievable efficiency given an percent deviation from
the imaginary part of the optimum load. We can set
(16) and set
can be approximated by
Rewriting (77) we have
We want to find the value of W in Fig. 14 that would maximize the field at a distance from the center of the coil. We can
maximize this value by maximizing (68) at (0, 0, ), hence
Hence the optimum outer edge follows:
Hence the loss in efficiency as a function of
is given by
F can also be represented in terms of Z parameters
In real world applications there are always some uncertainty
and time variance associated with the real and imaginary parts of
the load. In addition to this, it is extremely difficult to accurately
predict the inductance of the fabricated coils or the thickness of
different biological layers. As a result our load would deviate
from the optimum load (16), (17). In this section we study the
effect of deviation from the optimum load. First we will consider
the change in the imaginary part of the load. The Efficiency of
the two-port for an arbitrary load in terms of Y parameters is
given by
It is interesting to note that the sensitivity decreases for larger
values of . In order to get an idea on how sensitive we are
, Fig. 17 uses the coils from the
with respect to changes in
first and the third case study, presented earlier to show the loss
deviates up to 50% from its
in achievable efficiency when
optimal value.
Fig. 17 confirms our expectation that the sensitivity to variations in
is much higher when dealing with low efficiency
coupling. Starting from (77) we can derive a similar set of equations for the percentage of loss in two-port efficiency due to the
variations in the real part of the parallel load
Fig. 18 shows the reduction in efficiency due to changes in
the real part of the load.
, the
Similar to the case where we had deviation in
structure is more sensitive at low coupling scenarios. However
the change in efficiency with the increase in the real part of the
Fig. 18. Loss in efficiency due to deviation of R from R
Fig. 19. Output voltage and the delivered power for coils G1/G2 from the earlier example assuming 100% rectifier efficiency.
load is not of any concern as larger real loads require less current and hence lower power to begin with. Therefore the system
should consider the possible lower bound on the real part of the
load. The next graph in Fig. 19 demonstrates the same concept
in terms of current consumption. The figure depicts the change
in voltage and delivered power as the circuits draw different
amounts of current from the supply. The data are calculated for
two different power levels of 0.5 W and 1 W at the input of the
two-port. Here we are considering the DC current provided to
the load by the rectifier. We also assumed that the rectifier has
100% efficiency and hence the ac resistive load seen from the
input of the rectifier is half the DC resistive load:
As you can see the optimum current is 2.64 mA and 1.86 mA
for 1 W and 0.5 W of input power respectively. The efficiency
degrades as the current consumption deviates from this value.
However the system would still be functional at lower current
values since the voltage and power levels meet the minimum
requirement of the circuit. Higher current values will result in
lower voltage and hence might not be operational. Therefore the
power transfer two-port should consider the worst case current
consumption. It also requires a circuit to clip the voltage at low
current consumption levels to protect the circuits. The next logical question at this point is how accurately can we predict the
optimum load. The uncertainty in the imaginary part of the optimum load can be associated with variations in the thickness
and electrical properties of the layers surrounding the two coils
as well as the inductance of the coils. In order to study the effect of the variations in the media we first used the coils G1/G2
from case study 3. The blood depth was reduced to 6 mm from
the original 9 mm while the total distance between the two coils
was kept at 10 mm. Simulation shows that the change in the
imaginary part of the load for the new setup is less than 0.5%.
Next we used case study 3 from [39]. In this case study the receiver is buried under 10 mm of various biological tissue such
as fat, skull and Dura. We increased the thickness of every biological tissue by 20% hence increasing the distance between the
two coils from 10 mm to 12 mm. We also increased the thickness of the brain layers underneath the CMOS coil by the same
20%. As the result, the imaginary part of the load deviated from
the predicted value by less than 1% which resulted in less that
1% deviation from maximum possible achievable efficiency in
the two-port. However the achievable efficiency dropped to 11%
from the original 18% due to the extra 2 mm of separation. In
conclusion, the main contributing factor in
is the variation in the inductor value. On-chip inductors have
around 5 percent variations [40], [41] which at weak coupling
situations can lead to 15% loss in power efficiency.
The above analysis studied the variation of efficiency with
changes in load. However they did not consider the effect of
matching networks. Insertion of matching networks between the
load and the two-port affects the load variations. These variations depend on the quality factor of the matching networks. In
all cases however if the real part of the load of the matching network is changed by 50% the change in the real part of the desired
load is less than 50%. But the new mismatched load would have
extra reactance. Whether or not the added imaginary part is critical depends on the initial susceptance of the desired load (17).
Nevertheless by changing the quality factor of the matching network the designer can control the undesired added susceptance.
However the change in real part is always attenuated which is
a desirable property. In an L-match network the quality factor
depends on the load and the desired impedance and therefore
the designer has no control over the value. But using Pi or T
matching networks the designer would be able to control the
quality factor of the matching network. Here we will show how
the desired load would change with variations in the load for the
case of an L-match network shown in Fig. 4. For simplicity we
will assume that the load is purely resistive. This network would
to the desired load value of
. Now if the
convert the load
, the new value
load changes by a factor of
of the desired load for case (a) is given by
which is always smaller than m and the added susceptance is
given by
For case (b) in Fig. 4 we have
The added susceptance is usually small enough to be ignored.
Nonetheless in cases where the optimal susceptance (17) is comparable to (84) or (86), one has to choose a Pi matching network
and adjust the quality factor.
The authors would like to thank CMC for fabrication support.
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Meysam Zargham (S’06) received the B.Sc. degree
from Sharif University of Technology, Tehran, Iran,
in 2005 and the M.Sc. degree in electrical engineering from the University of Alberta, Edmonton,
AB, Canada in 2008.
He is currently working toward the Ph.D. degree
at the University of Toronto, Toronto, ON, Canada,
where his research is in the area of CMOS integrated
circuits for biomedical applications. He was a
member of the icore High Capacity Digital Communications Laboratory. While working toward the
M.Sc. degree, he was involved in many different projects in a variety of groups,
including the design of analog LDPC decoders, micro-fluidic lab-on-a-chip
design, and the modeling of carbon nanotube transistors.
P. Glenn Gulak (S’82–M’83–SM’96) received the
Ph.D. degree from the University of Manitoba, Winnipeg, MB, Canada.
While at the University of Manitoba, he held
a Natural Sciences and Engineering Research
Council of Canada Postgraduate Scholarship. He is
a Professor with the Department of Electrical and
Computer Engineering, University of Toronto, ON,
Canada, as well as a registered Professional Engineer
in the Province of Ontario. His present research interests are currently focused on algorithms, circuits,
and system-on-chip architectures for digital communication systems; and for
biological lab-on-chip microsystems. He has authored or coauthored more than
100 publications in refereed journal and refereed conference proceedings. In
addition, he has received numerous teaching awards for undergraduate courses
taught in both the Department of Computer Science and the Department of
Electrical and Computer Engineering at the University of Toronto. He held the
L. Lau Chair in Electrical and Computer Engineering for the five-year period
from 19992004. He currently holds the Canada Research Chair in Signal
Processing Microsystems and the Edward S. Rogers Sr. Chair in Engineering.
From January 1985 to January 1988, he was a Research Associate in the
Information Systems Laboratory and the Computer Systems Laboratory at
Stanford University, Stanford, CA. From March 2001 to March 2003, he was
the Chief Technical Officer and Senior Vice President of LSI Engineering, a
fabless semiconductor startup headquartered in Irvine, CA with $70M USD of
financing that focused on wireline and wireless communication ICs
Dr. Gulak served on the ISSCC Signal Processing Technical Subcommittee
from 1990 to 1999, was ISSCC Technical Vice-Chair in 2000, and served as
the Technical Program Chair for ISSCC 2001. He was the recipient of the IEEE
Millennium Medal in 2001. He currently serves on the Technology Directions
Subcommittee for ISSCC and as Editor-at-Large for ISSCC 2012.