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IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 34, NO. 12, DECEMBER 1999
A CMOS-Only Micro Touch Pointer
Nicolò Manaresi, Roberto Rambaldi, Marco Tartagni,
Zsolt Miklós Kovács Vajna, Member, IEEE, and Roberto Guerrieri
Abstract— A direct-contact finger mouse realized in 0.7-m
digital CMOS is presented. It is based on the motion detection of
the fingerprint images acquired with a capacitive sensor. Stroking
and tapping the chip surface with the finger causes movement
of the cursor and clicking-like mouse. By properly partitioning
analog collective computation and digital processing, a power
consumption of about 900 W at 5 V is achieved. The sensor
area is 3.8 2 3.8 mm2 , and overall chip size is 7.7 2 6.7 mm2 .
Index Terms— Capacitive fingerprint sensors, image motion
analysis, intelligent sensors, mixed analog–digital integrated circuits, user interfaces.
I. INTRODUCTION
I
N THE emerging market of portable computing, miniaturization has led to systems with unprecedented small
form factors. The computing power made available by modern
CMOS technologies allows new features and capabilities to
be embedded in devices that fit into the palm of the user.
Eventually they will be on the user’s wrist or even, with
credit-card-size computers, into his wallet. The design of new
interfaces to devices of ever shrinking size undoubtedly poses
a challenge. Voice input is a possible solution for data entry,
as adopted by “voice organizers.” However, other functions,
such as Web browsing, need a display and pointing capability.
Pointing devices for such applications should satisfy key
requirements such as low cost, small form factor, relatively
high resolution, and robustness.
A few interface solutions have been available to laptop
and notebook manufacturers. A touch pad [1], for example,
is an excellent alternative to a mouse. The touch pad has a
large area available for fingertip movement and allows the
direct implementation of features like clicking by tapping the
finger on the sensitive area. However, the large surface area
of the touch pad prevents its use when dimensions become
comparable to those of the embedding system (e.g., a palm-top
computer or a cell-phone handset). Other solutions are track
balls and joysticks small enough to fit in the middle of the
keyboard. Even though their dimensions are much smaller,
they involve mechanical moving parts, affecting robustness
and durability. Palm-top computers use a different approach
by adopting a touch screen; however, this requires an external
pen-like tool in order to enter data and to select icons on the
screen itself. With an even smaller available space, cell-phone
Manuscript received July 5, 1999; revised August 30, 1999. This work was
supported by STMicroelectronics.
N. Manaresi, R. Rambaldi, M. Tartagni, and R. Guerrieri are with D.E.I.S.,
University of Bologna, Bologna 40136 Italy.
Z. M. K. Vajna is with the Department of Electrical Engineering, University
of Brescia, Brescia 25123 Italy.
Publisher Item Identifier S 0018-9200(99)09268-9.
mobile terminals use arrow-type navigation aids for browsing
menus and moving the cursor along the display (e.g., for
editing purposes). Repeatedly pressing one or more buttons
allows the user to move the pointer step by step of a fixed
amount over the display. However, for increasing resolution
and faster displacements of the cursor, other solutions would
be advisable.
The idea of using smart sensors for pointing devices,
integrating sensing and processing on a single chip, is not
new [2]. Several approaches, based on correlation of images
sampled at different times [3], on edge detection and tracking
[4], or on analog brightness gradient intensity [5] have been
previously reported. All the implementations rely on optical
image sensors, and although the constraints on images may
vary, they inevitably involve some optical parts such as lenses
and, sometimes, light-emitting diodes, affecting the form factor
and power consumption of the system.
On the contrary, the pointing device presented in this paper
relies on capacitive fingerprint images. With this approach, the
whole pointing system can be realized on a single standard
CMOS chip, without mechanical moving parts or optical addons. The pointer behaves as a track ball, and the detection of
the presence of a finger on the sensor allows implementation
of the click feature as commonly done in a touch pad.
This paper is organized as follows. Section II reports an
overview of the implemented algorithm and discusses the
design constraints and specifications involved in the detection
of the finger movement. In Section III, the architecture of the
touch pointer system is described along with details of the
circuit implementation and the solutions adopted to overcome
the stringent systems constraints. Comprehensive test results
are then reported in Section IV, while in Section V, some
conclusions are drawn.
II. THE TOUCH POINTER APPROACH
The movement of the finger is detected by analyzing the
motion of the pattern generated by ridges and valleys on sensor
cells based on the capacitive feedback principle [6]. The task
requires considerable computing power, and some special solutions are required at the algorithmic, architecture, and circuit
level to obtain a power-efficient monolithic implementation.
A. Algorithm Overview
Since the information of interest is the pattern motion and
not the pattern itself, a major step is taken at the algorithmic
level to reduce the data to be processed, limiting the sampling
of the fingerprint pattern to an array of 7 7 sites, as shown
in Fig. 1. For each site, the image is sampled in five pixels,
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Fig. 1. Algorithm overview.
arranged as a cross. For each pixel, a binary value is computed
at each time step, which is asserted if the pixel output is
larger than the average of a given amount. Furthermore, only
low-to-high transitions are considered.
For each cross, a finite state machine (CFSM) detects
whether a ridge has passed, and in which direction. Lowto-high transitions in the central pixel trigger the CFSM.
Subsequent events in either right or left (top or bottom) pixels
detect a movement in that direction, while the CFSM is reset
to an idle state for that axis. Simultaneous events in both left
and right (top and bottom) pixels just reset the cross to the
idle state without detecting any movement. Every time frame,
each CFSM, independently for and , reports a digital value
, which is 0, 1 or 1 depending on whether the cross
reports no movement or a movement in the positive or negative
direction for that axis, respectively. The overall displacement
and
is then computed by combining local information
provided by the crosses. The local displacements are summed
up, and the sign of the result determines if the pattern has
), in the
moved one pixel in the negative direction (
) direction, or not at all (
) along that
positive (
axis. Accordingly, the global displacement in pixels can be
expressed as
sign
sign
(1)
Cross redundancy helps to attenuate the effects of ridges
roughness, skin elasticity, and friction, which can cause, in
. With the proposed algorithm,
a single cross, wrong
considering a sensor area of 3.8
3.8 mm and a cell pitch
of 65 m, the number of pixels is reduced approximately by
92% (from 3300 to 245) with respect to a full image solution.
B. Design Constraints and Specification
According to the outlined algorithm, the capacitive images
must be sampled at a rate such that two subsequent frames
are displaced by at most one pixel. Spatial resolution sets
an upper bound on the sensor’s cell pitch, which is chosen
to be 65 m. Assuming a peak fingerprint speed
m/s, the minimum time required by features of the pattern
to travel from one cell to the neighboring one is
cellpitch
s. Therefore, in order to properly detect
the motion, a frame rate greater than 15 kFrames/s should be
achieved, which is about three orders of magnitude larger than
implementations reported to date [6], [7]. This number rules
out the possibility of using the conventional sequential readout,
even for a low number of pixels, mandating a parallel solution.
Additional problems arise from the variability of the images
captured by the sensor under different conditions. Among the
main challenges posed by the detection of the fingerprint, we
can list the following.
1) User-Dependent Finger Dryness: The condition of the
skin surface influences the output of the capacitive cells, so
that for a dry finger, the contrast tends to get worse and the
common mode may vary. As a consequence, two users may
generate capacitive images of different quality. This may also
happen with a single user depending on objective (e.g., room
temperature) or subjective (e.g., after physical activity) factors.
2) Time-Dependent Finger Dryness: The increase of moisture on the skin pressed over the sensor (due to the lack of
evaporation) produces an image with a time varying contrast.
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(a)
(b)
(c)
Fig. 2. Fingerprint images taken with a capacitive sensor [6]: (a)–(c) histogram of gray-level values of pixels, gray-level image, and segmented image
under different finger conditions, respectively.
3) Dirt and Dust: The image can be corrupted by speckles
unrelated with the finger movement such as single pixels stuck
at low or high values due to dirt on the sensor.
Fig. 2 reports the result of some measurements, made with
the capacitive sensor [6], illustrating the aforementioned problems. For each acquisition, the histogram of gray-level values,
the gray-level image, and the image segmented according to
the algorithm are reported. The vertical dashed lines in the
histograms correspond to the average gray-level (Avg) and
corresponding segmentation threshold (Th), which is offset
by one-eighth of full scale. Fig. 2(a), which is taken with a
moderately moist finger on a clean sensor, exhibits a good
contrast: the bimodal distribution of ridges’ and valleys’ gray
levels makes it easy to convert it into a binary image. The
contrast gets worse when a dry finger is applied, as shown in
Fig. 2(b). When, following a repeated touching, a thin grease
layer is deposited over the sensor, a dry finger generates
even more difficult images, as shown in Fig. 2(c). Ridges
(the brighter part) spread into a wide range of gray levels.
Moreover, the average value reduces to less than 30% of the
original value. Linking the threshold to the average allows
us to compensate for these common-mode variations. The
segmentation threshold is actually offset from the average so
as to generate a clean image when no finger is applied on the
sensor, as shown in Fig. 2(d) and (e), taken with air and moist
air, respectively.
III. CHIP ARCHITECTURE
At the architecture level, the partitioning of analog and
digital processing is the key to achieving low power consumption. Despite the reduction of the number of pixels,
an approach using analog-to-digital (A/D) conversion on the
cells’ output voltage would result in the processing of a
large amount of data, even with a moderate resolution (e.g.,
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(d)
(e)
Fig. 2.
(Continued.) Fingerprint images taken with a capacitive sensor [6]: (d) with air and (e) with very humid air.
6 bits, so as to provide enough dynamic range). With a
sequential readout, a 6-bit A/D converter at about 245/65 s
3 Msamples/s would be required to digitize the image.
As proposed in the algorithm, for each frame one should
compute the average pixel intensity (requiring 245 sums and
one division) and then compare each pixel with the average
(other 245 operations) in order to produce the binary image,
which is processed to detect the motion. Therefore, at least
2/65 s
7.5 MOPS would be required for image
245
preprocessing.
In order to reduce consumption, an approach using analog
collective computation for early image processing is used,
instead of a purely digital approach. A parallel read-out is
chosen for the cells, and a distributed switched-capacitor
circuit globally computes the average cell output. In this way,
the dynamic range requirements on the A/D conversion in
order to detect the ridges are dramatically relaxed. Therefore,
a simple comparator is sufficient to obtain the desired binary
image of the fingerprint (corresponding to segmented images
in Fig. 2).
A block diagram of the chip is reported in Fig. 3. The
analog core samples the fingerprint image and preprocess it
as described before, producing the binary image (OutBit ).
The testing block allows one to read the OutBit , in order
to test the analog core, and to write them for testing the
digital part. During normal operation, the OutBit bypass the
testing block and are fed to the CFSM’s. Each CFSM computes
the displacement for the corresponding cross. The results are
combined by the motion processor, which computes the global
displacement for the current frame and accumulates it with
the previous ones, until the external microprocessor reads the
result, approximately every millisecond.
A. Analog Core
Fig. 4 reports the schematic of one pixel of the array and
the associated circuitry to produce a binary value, while the
measured waveforms are shown in Fig. 5. When the skin
approaches the sensor surface it modifies the fringing field
between the two metal-2 plates facing the finger, reducing
the effective feedback capacitance of the charge amplifier.
The output voltage variation
associated to the input
, occurring after reset phase , is greater for
voltage step
cells under ridges than for cells under valleys. Capacitors other
than the sensing one are metal-1–to-metal-2 (Ci) or poly-tometal (C1) parasitics. The circuit operates in weak inversion to
minimize power consumption. All the pixels work in parallel:
is fed to two identical charge amplifiers
for each of them,
with unity gain. The first has both its virtual ground input
and the output connected with those of other pixels, which
collectively compute
(2)
The second just inverts and shifts the cell output, yielding
. The output of the clocked comparator
OutBit is therefore high only if
(3)
The schematic of the comparator featuring a zero static-power
dissipation is shown in Fig. 6. The CMOS inverters equalize
the capacitive load at the output of the differential couple and
buffer the digital output signal. OutBit are also used to detect
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IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 34, NO. 12, DECEMBER 1999
Fig. 3. Chip architecture.
Fig. 4. Analog core: pixel-level schematic.
whether a finger is present on the sensor. A Finger Valid
signal is asserted if
OutBit
(4)
typically set to 5% N. The Finger Valid allows
with
one to implement the click without the need of a button.
The parallel evaluation of the cells presents some drawbacks, from the standpoint of pixel interference. In fact, as
shown in Fig. 7, the outputs of neighboring capacitive cells
experience a large voltage swing, which can couple to the
through the parasitic capacitances. This
virtual ground node
unless some steps
interference can corrupt the cell signal
are taken to minimize the parasitics involved. First of all, the
routing of the output signal is shielded by a grounded metal-1
plate whenever crossing the metal-2 input plates. In order to
Fig. 5. Analog core: pixel-level measured waveforms.
minimize also the fringing capacitance from the output node to
the input of neighboring cells, the output plate is surrounded
by the input one, as shown in Fig. 8.
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Fig. 6. Comparator schematic diagram.
Fig. 9. Finger-conveyed power-network interference.
variation of
the frame time
during the frame cycle matters. Assuming
, the maximum variation during
is
(5)
Fig. 7. Cross-capacitance influence in parallel evaluation.
Hz,
V,
s, one
Substituting
mV, which is one order of magnitude lower
gets
than . The longer the time frame, the larger the swing on
. In fact, this can be a major source of noise for frame
times of 100 s or more, although this basically translates to
a common-mode signal.
B. Digital Core
Fig. 8. Layout of the top-level metal of the cell to reduce pixel
cross-coupling.
Parallel evaluation allows us to relax the pixel rate to the
frame rate and reduce power consumption, since the bandwidth required by the cell inverters can be achieved in weak
inversion. On the other hand, the long evaluation time can
cause problems because of finger-conveyed power-network
interference. As shown in Fig. 9, the voltage variation
on the fingertip, due to the power-network electromagnetic
interference, adds to the input charge through the parasitic
capacitance
- between the finger and the input plate.
The estimated values of the metal-1–metal-2 input capacitance
and of
- have the same order of magnitude, while
(typically 300 mV) can be one order of magnitude lower
than the peak-to-peak skin voltage
. However, only the
The main blocks of the digital processor are shown in
Fig. 3. On the top side of the figure, the sensor array faces
a datapath that comprises a testing block, the CFSM’s, and
the motion processor. The datapath is pixel-sliced down to
the CFSM’s and cross-sliced between CFSM’s and the motion
processor. These blocks communicate by means of an internal
data/control bus with other structures: the waveform generators, the chip controller, the microprocessor interface, and the
register file.
In the CFSM’s, the current frame is compared with the
previous one in order to detect the significant pixel-level
events, i.e., OutBit low-to-high transitions. These events
drive the state of the CFSM’s, which compute the local
displacements for the current cycle. The motion processor then
computes the global displacement according to (1) and the
Finger Valid according to (4).
The behavior of the digital core is managed by the chip controller, which schedules all the functionality of the device; a
programmable wave generator that regulates the functionality
of the analog sensor; and a distributed register file that holds
all the information and parameters regarding the computation
and the wave generation. Finally, the computed displacements
or the testing data are driven to the output by means of a
microprocessor interface. The testing unit has the fundamental
task of driving most of the internal digital states, if requested,
into the same data bus of the output displacements. Therefore,
the following functionalities are provided: grab of digital
images from the analog array, check of the CFSM’s status,
and check of the motion processor.
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Fig. 10.
IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 34, NO. 12, DECEMBER 1999
Operations of the digital processor.
Referring to Fig. 10, the device operation is composed of
the following states.
• Idle: This state is entered at the end of each frame
cycle. Until the input Stop signal is high, the chip keeps
into this state, setting the output Busy low. During this
phase, all the digital blocks are turned off, and the sensor
array is fed by signals that keep it into an idle lowpower consumption state. The Idle phase can be used
for reading the finger movement or to write the register
file for configuration purposes.
• Phase Generation: As soon as Stop goes low,
the state changes from Idle to Phase Generation,
and the waveform generators provide the analog control
, STROBE) for one frame cycle.
signals (
• Compute: If no Test cycle is needed, after the Delay
phase, the motion processor is fired.
• Test: At the end of the Delay phase, if the
Test Enable is high, the chip freezes the analog
operation. This allow one to scan out the OutBit image.
Clock gating has been widely used, in order to reduce the
switching noise during analog processing and to reduce the
overall power consumption. Each block is fed by a gated clock,
whose activation is enabled by the controller and disabled by
the block itself. Since digital computation takes place in 3.5 s,
analog and digital operations can be time-multiplexed. During
sensor evaluation and analog averaging, only the waveform
generators are active, thus reducing substrate coupling effects.
IV. TEST RESULTS
A. Pointing
Fig. 11(a) shows four frames (temporally spaced by 67.8
ms, left to right) of the NE-to-SW sweep of a plastic pattern. This experiment partially reproduces the effect of skin
plasticity but allows one to follow the image over the sensor,
whereas a fingerprint pattern would be too complex due to the
high number of ridges and valleys in the sensor area (as seen
in Fig. 1). Only the pixels that correspond to OutBit
are drawn, in black. The corresponding trajectory and other
examples computed by the chip are plotted in Fig. 11(b).
This result shows that the direction of the movement is
clearly identified, although the absolute value of the computed
displacement varies. In fact, the displacements computed at
(a)
(b)
Fig. 11. (a) Four binarized frames from NE-to-SW sweep and (b) displacement computed by the chip with a synthetic pattern.
two crosses are counted only once if they occur during
the same frame, or twice if occurring in subsequent ones.
However, the device will be inserted into a visual feedback
loop with the user that corrects the above imprecision.
In Fig. 12, the touch pointer is tested in the loop with the
user. The benchmark task of connecting the central square with
the peripheral ones has been chosen because of its similarity
to pointing to different buttons, as it is common in graphical
user interfaces. It is apparent that, thanks to the user visual
feedback, the task can be carried out correctly. This data
were collected by connecting the test board to a PC, with
a standard mouse driver. Although a specialized driver could
smooth the trajectories, this would probably be unnecessary
for many applications. The device is operated like a track ball.
Few strokes are needed to reach the target, with a one-to-one
mapping between chip output and pixel displacement. In this
example, the square is 40 pixel wide, with a pitch of 140
between them. The overall window would be about 320
320, which is a reasonable number when dealing with tiny
portable computers or cellphones.
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TABLE I
CHIP PERFORMANCES
Fig. 12. A benchmark test with the user: connect the central square with
peripheral ones.
Fig. 14. Chip photograph.
(a)
(b)
Fig. 13. Click feature: (a)
tapping the chip.
j
OutBitj and (b) Finger Valid, when
B. Clicking
is used
As described before, the number of OutBit
to provide a Finger Valid signal. When the user taps the
sensor, the Finger Valid is asserted for a shorter time,
compared to strokes. The length of Finger Valid is used
to implement the click feature, by detecting the tapping, as
commonly done in touch pad. Fig. 13(a) shows the temporal
OutBit when a finger is tapped on the chip.
evolution of
The resulting Finger Valid signal is reported at the bottom
of Fig. 13. The glitches are easily filtered by the external
microprocessor.
C. Performance Summary
The chip is realized in a 0.7- m, one-poly, two-metal,
65 m ,
digital CMOS technology. The pixel size is 65
corresponding to a maximum resolution of 390 dpi. However,
actual resolution depends on the complexity of the pattern.
A lower bound can be computed considering the worst case
of a unique straight line of white pixels crossing the sensor
in a perfectly horizontal (or vertical) direction. In this case,
all the crosses in one column report a displacement
at the same time. The integral displacement computed for
the whole crossing of the sensor width is then equal to the
number of columns. The minimum resolution is therefore 7/3.8
[dots/mm], which is equivalent to 46 [dots/in]. The actual
resolution is typically higher, five times or more, due to the
presence of multiple ridges on the sensor area. The power
consumption is dominated by the analog core, which dissipates
900 W from a 5-V power supply. Chip performances are
summarized in Table I.
The photograph of the chip is reported in Fig. 14. The
analog average circuitry and the comparators find their place in
the space between the crosses of capacitive cells. The testing
circuitry allows one to read/write the OutBit , which are
the boundary signals between analog and digital processing.
Testing of the analog and digital part can thus be done
separately. Furthermore, a scanner for the analog values of the
pixels is introduced for a thorough testing of the analog core.
Generous space from the analog core to the top and bottom
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pad rings is required to ensure that the thickness of the epoxy
glue, which protects the bonding wires, does not impede a full
contact of the finger with the sensor cells.
V. CONCLUSION
A pointing device targeted to portable applications has been
presented. The system does not involve mechanical moving
parts, nor optical components, and is fabricated as a low-cost,
monolithic standard-CMOS chip in a 0.7- m technology. The
system is based on fingerprint motion detection. When the user
touches the chip, his or her fingerprint pattern is grabbed with
an array of capacitive sensors, and the motion of the pattern
is detected by a mixed A/D circuit.
The reduction of power consumption has been tackled at
different abstraction levels. At algorithmic level, the data to
be processed are reduced by 92% by sampling the fingerprint
pattern in an array of crosses and by keeping straightforward
the motion detection with simple finite state machines. At
architecture level, processing in analog and digital domain is
carefully partitioned. Parallel readout and analog preprocessing
allow us to reduce the dynamic range requirements so that a
1-bit A/D conversion is enough to extract the relevant information. At circuit level, power consumption is reduced by weak
inversion operation of the continuous-time analog circuits and
by clock gating inactive digital circuits. The resulting power
consumption is about 900 W from a 5-V power supply.
ACKNOWLEDGMENT
The authors would like to thank D. Ercolani and M. Bisio
for their help with the test board and A. Kramer.
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Nicolò Manaresi was born in Italy in 1967. He
graduated in electrical engineering and computer
science and received the Ph.D. degree in electrical
engineering from the University of Bologna, Italy,
in 1993 and 1999, respectively.
From 1993 to 1995, he was with the Department
of Electrical Engineering, University of Bologna,
first availing himself of a grant from and then as
a Consultant for ST-Microelectronics for the design
of analog fuzzy circuits. In 1995, he spent one year
at the Swiss Federal Institute of Technology, Zürich,
were he was involved in the design of CMOS RF analog circuits. His research
interests are in the areas of integrated sensors and analog circuit design.
Roberto Rambaldi was born in Bologna, Italy, in
1969. He received the Dr.Eng. degree in electrical
engineering and the Ph.D. degree from the University of Bologna in 1994 and 1998, respectively.
His research was on low-power ASIC’s for image
processing. His main topics of interest are embedded
digital processors for CMOS sensors, low-power
digital design, and fast prototyping of digital systems.
Marco Tartagni received the five-year degree and
the Ph.D. degree from the University of Bologna,
Italy, in 1988 and 1993, respectively, both in electrical engineering.
His research was on CMOS camera design. He
joined the Department of Electrical Engineering of
the California Institute of Technology, Pasadena, in
1992 as a Visiting Student and in 1994 as a Research
Fellow, working on various aspects of analog VLSI
for image processing. Since 1995, he has been a
Research Associate at the Department of Electronics, University of Bologna, where he focused his research on the design of
advanced sensor architectures such as a direct-contact capacitive fingerprint
sensor. His current research interests include electric-field surface sensors,
optical sensors, and silicon systems for biological object manipulation.
Zsolt Miklós Kovács Vajna (M’90) received the
laurea degree and the Ph.D. degree in electrical engineering and computer sciences from the University
of Bologna, Italy, in 1988 and 1994, respectively.
From 1989 to 1998, he was with the Department of Electrical Engineering of the University of
Bologna, where his research was on optical character recognition and circuit simulation techniques.
From 1994 to 1998, he was an Assistant Professor
and Research Associate in electronics. In 1998, he
joined the Department of Electrical Engineering of
the University of Brescia, Italy. He is currently an Associate Professor in
electronics and teaches the course on microelectronics.
Dr. Vajna is a member of the International Association for Pattern Recognition and the Pattern Recognition Society.
Roberto Guerrieri received the Dr.Eng. degree and the Ph.D. degree in
electrical engineering from the University of Bologna, Italy, in 1980 and
1986, respectively.
From 1980 to 1986, he was with the Department of Electrical Engineering,
University of Bologna. His research was on the numerical simulation of
semiconductor devices. From 1986 to 1988, he was with the Department
of Electrical Engineering and Computer Sciences, University of California,
Berkeley, as a Visiting Researcher. In 1987, he spent the winter semester at the
Massachusetts Institute of Technology, Cambridge, as a Visiting Scientist. In
1989, he joined the University of Bologna, where he is currently an Associate
Professor in charge of the Laboratory for VLSI design. His research interests
are in various aspects of applied pattern recognition, integrated circuit design,
and parallel processing.
In 1986, he received a NATO fellowship. In 1989, he received a fellowship
for young researchers from Consiglio Nazionale delle Ricerche, Italy. In
1992, he won the Best Paper Award from the IEEE TRANSACTIONS ON
SEMICONDUCTOR MANUFACTURING.