ELCT 891Y: Sensors
Dr. Goutam Koley
Room 3A12, 777-3469, koley@engr.sc.edu
Lecture Hours: Mon & Wed 4.00 PM – 5.15 PM
300 M, B101
Office Hours: By appointment only
Other information: To be posted on the website
Slide # 1
Course Information
Objective: To learn principles of modern sensors and
actuators (emphasis on micro and nanoscale sensors)
Reference book:
Handbook of Modern Sensors: Jacob Fraden, Fourth Edition, Springer.
ISBN: 978-1-4419-6465-6
Other notes and handouts will be given from time to time, or
references posted on the course website
Slide # 2
Schedule and Grading
Class: January 9 – April 23, 28 lecture days
Final Exam Week: April 25 – May 2, 2012
Grading:
Midterm:
Final:
Project and presentation
Approximate Grades:
A
B+
B
C+
C
D+
D
F
30 %
30 %
40 %
90 - 100
85 - 89
80 - 84
75 - 79
70 – 74
65 – 69
60 – 64
<60
Slide # 3
Class schedule
•
•
•
•
Lectures: 18 Classes
Midterm: 1 Class
Revision: 2 Classes
Student Presentations (2 sets): 8 Classes
A project report has to be submitted by each of the
students at the end of the semester on the last day of
classes, i.e. on April 23.
Slide # 4
Course Contents 1
 Sensor characteristics (1)
 Position and Displacement sensors (2)
 Positions sensors
 Displacement sensors
 Level sensors
 Velocity and Acceleration sensors (2)
 Velocity sensing
 Acceleration sensing
 Force and Strain sensors (2)
 Force sensors
 Strain sensors
Slide # 5
Course Contents 2
 Pressure and Flow sensors (1)





 Pressure sensors
 Flow sensors
Acoustic sensor (1)
Humidity sensor (1)
Radiation sensor (1)
Chemical Sensors (3)
Microcantilevers as sensing elements (2)
 Basics of microcantilever based sensing
 Applications in sensing
 Nanoscale sensors and applications (1)
 Nanowire based sensors
 CNT and Graphene based sensors
Slide # 6
Project topics
• List of projects
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Automotive accelerometer
Explosive detection systems
Mass flow and pressure sensors/controllers
Cooled and Uncooled IR sensors
Nanoscale bio-sensors for pathogen detection
MEMS gyroscopes for navigation
RFID based temperature and humidity sensors
Strain sensors for prognostics in airplanes and bridges
Volatile organic compound sensors
Automotive O2 and NOx sensors
MEMS/NEMS sensors for pathogen and toxic gases
Slide # 7
Sensor Characteristics
Transfer Function:
The functional relationship between physical input signal and electrical output signal.
Usually, this relationship is represented as a graph showing the relationship between
the input and output signal, and the details of this relationship may constitute a
complete description of the sensor characteristics.
Examples of Transfer function:
Linear: Vout = A + Bs
Non-linear: Vout = Beks = B(1+ks+k2s2/2+..)
Here s is the input signal
and Vout is the electrical output
A non-linear transfer function can be linearized using piecewise approximation
Slide # 8
Sensor Characteristics
Sensitivity:
The sensitivity S is defined as the ratio between a small change in electrical
signal to a small change in physical signal. As such, it may be expressed as the
derivative of the transfer function with respect to physical signal.
Mathematically, it is given as: S 
dVout
ds
For a linear transfer function such as Vout = A + Bs, the sensitivity would simply
be B.
For a non-linear transfer function, the sensitivity would vary with the particular
segment of the linearization.
Slide # 9
Sensor Characteristics
Span or Dynamic Range:
The range of input physical signals which may be converted to electrical signals by
the sensor. Signals outside of this range are expected to cause unacceptably large
inaccuracy. This span or dynamic range is usually specified by the sensor supplier
as the range over which other performance characteristics described in the sheets
are expected to apply.
Hysteresis:
Some sensors do not return to the
same output value when the input
stimulus is cycled up or down.
The width of the expected error in
terms of the measured quantity is
defined as the hysteresis. Typical
units: % of FSO
Slide # 10
Sensor Characteristics
Accuracy:
Generally defined as the largest expected error between actual and ideal output
signals. Sometimes this is quoted as a fraction of the full scale output.
Slide # 11
Sensor Characteristics
Non-linearity:
The maximum deviation from a linear transfer function over the specified dynamic range.
There are several measures of this error. The most common compares the actual transfer
function with the `best straight line', which lies midway between the two parallel lines which
encompasses the entire transfer function over the specified dynamic range of the device. This
choice of comparison method is popular because it makes most sensors look the best.
Slide # 12
Sensor Characteristics
Noise:
All sensors produce some output noise in addition to the output signal. In some
cases, the noise of the sensor is less than the noise of the next element in the
electronics, or less than the fluctuations in the physical signal, in which case it is not
important.
Noise is generally distributed across the frequency spectrum. Many common noise
sources produce a white noise distribution, which is to say that the spectral noise
density is the same at all frequencies. Johnson noise in a resistor is a good
example of such a noise distribution.
For white noise, the spectral noise density is characterized in units of Volts/(Hz). A
distribution of this nature adds noise to a measurement with amplitude proportional
to the  (Measurement bandwidth B). Since there is an inverse relationship between
the bandwidth and measurement time, it can be said that the noise decreases with
the square root of the measurement time.
Slide # 13
Sensor Characteristics
Resolution:
The resolution of a sensor is defined as the minimum detectable signal fluctuation. Many
sensors are limited by noise with a white spectral distribution. In these cases, the resolution
may be specified in units of (physical signal)/(Hz).
Sensor data sheets generally quote resolution in units of signal/(Hz) or they give a minimum
detectable signal for a specific measurement. The actual resolution for a particular
measurement may be obtained by multiplying this quantity by the square root of the
measurement bandwidth.
Thus, the lower the bandwidth, the higher is the resolution.
Bandwidth:
All sensors have finite response times to an instantaneous change in physical signal. In
addition, many sensors have decay times, which would represent the time after a step
change in physical signal for the sensor output to decay to its original value. The reciprocal of
these times correspond to the upper and lower cutoff frequencies, respectively. The
bandwidth of a sensor is the frequency range between these two frequencies.
Slide # 14