Smart Sensor Systems -  - E-Book

Smart Sensor Systems E-Book

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With contributions from an internationally-renowned group ofexperts, this book uses a multidisciplinary approach to reviewrecent developments in the field of Smart Sensor Systems, coveringimportant system and design aspects. It examines topics overthe whole range of sensor technology from the theory andconstraints of basic elements, physics and electronics, up to thelevel of application-orientated issues. Developed as a complementary volume to 'Smart SensorSystems' (Wiley 2008), which introduces the basics of smartsensor systems, this volume focuses on emerging sensingtechnologies and applications, including: * State-of-the-art techniques for designing smart sensors andsmart sensor systems, including measurement techniques at systemlevel, such as dynamic error correction, calibration,self-calibration and trimming. * Circuit design for sensor systems, such as the design ofprecision instrumentation amplifiers. * Impedance sensors, and the associated measurement techniquesand electronics, that measure electrical characteristics to derivephysical and biomedical parameters, such as blood viscosity orgrowth of micro-organisms. * Complete sensor systems-on-a-chip, such as CMOS optical imagersand microarrays for DNA detection, and the associated circuit andmicro-fabrication techniques. * Vibratory gyroscopes and the associated electronics, employingmechanical and electrical signal amplification to enable low-powerangular-rate sensing. * Implantable smart sensors for neural interfacing in bio-medicalapplications. * Smart combinations of energy harvesters and energy-storagedevices for autonomous wireless sensors. Smart Sensor Systems: Emerging Technologies and Applicationswill greatly benefit final-year undergraduate and postgraduatestudents in the areas of electrical, mechanical and chemicalengineering, and physics. Professional engineers and researchers inthe microelectronics industry, including microsystem developers,will also find this a thorough and useful volume.

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Table of Contents


Title Page


About the Editors

Gerard Meijer

Michiel Pertijs

Kofi Makinwa

List of Contributors


Chapter 1: Smart Sensor Design

1.1 Introduction

1.2 Smart Sensors

1.3 A Smart Temperature Sensor

1.4 A Smart Wind Sensor

1.5 A Smart Hall Sensor

1.6 Conclusions


Chapter 2: Calibration and Self-Calibration of Smart Sensors

2.1 Introduction

2.2 Calibration of Smart Sensors

2.3 Self-Calibration

2.4 Summary and Future Trends


Chapter 3: Precision Instrumentation Amplifiers

3.1 Introduction

3.2 Applications of Instrumentation Amplifiers

3.3 Three-OpAmp Instrumentation Amplifiers

3.4 Current-Feedback Instrumentation Amplifiers

3.5 Auto-Zero OpAmps and InstAmps

3.6 Chopper OpAmps and InstAmps

3.7 Chopper-Stabilized OpAmps and InstAmps

3.8 Chopper-Stabilized and AZ Chopper OpAmps and InstAmps

3.9 Summary and Future Directions


Chapter 4: Dedicated Impedance-Sensor Systems

4.1 Introduction

4.2 Capacitive-Sensor Interfaces Employing Square-Wave Excitation Signals

4.3 Dedicated Measurement Systems: Detection of Micro-Organisms

4.4 Dedicated Measurement Systems: Water-Content Measurements

4.5 Dedicated Measurement Systems: A Characterization System for Blood Impedance

4.6 Conclusions


Chapter 5: Low-Power Vibratory Gyroscope Readout

5.1 Introduction

5.2 Power-Efficient Coriolis Sensing

5.3 Mode Matching

5.4 Force Feedback

5.5 Experimental Prototype

5.6 Summary


Chapter 6: Introduction to CMOS-Based DNA Microarrays

6.1 Introduction

6.2 Basic Operation Principle and Application of DNA Microarrays

6.3 Functionalization

6.4 CMOS Integration

6.5 Electrochemical Readout Techniques

6.6 Further Readout Techniques

6.7 Remarks on Packaging and Assembly

6.8 Concluding Remarks and Outlook


Chapter 7: CMOS Image Sensors

7.1 Impact of CMOS Scaling on Image Sensors

7.2 CMOS Pixel Architectures

7.3 Photon Shot Noise

7.4 Analog-to-Digital Converters for CMOS Image Sensors

7.5 Light Sensitivity

7.6 Dynamic Range

7.7 Global Shutter

7.8 Conclusion



Chapter 8: Exploring Smart Sensors for Neural Interfacing

8.1 Introduction

8.2 Technical Considerations for Designing a Dynamic Neural Control System

8.3 Predicate Therapy Devices Using Smart-Sensors in a Dynamic Control Framework: Lessons Derived from Closed-Loop Cardiac Pacemakers

8.4 The Application of “Indirect” Smart Sensing Methods: A Case Study of Posture Responsive Spinal Cord Stimulation for Chronic Pain

8.6 Future Trends and Opportunities for Smart Sensing in the Nervous System



Chapter 9: Micropower Generation: Principles and Applications

9.1 Introduction

9.2 Energy Storage Systems

9.3 Thermoelectric Energy Harvesting

9.4 Vibration and Motion Energy Harvesting

9.5 Far-Field RF Energy Harvesting

9.6 Photovoltaic

9.7 Summary and Future Trends



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Table of Contents

List of Illustrations

Figure 1.1

Figure 1.2

Figure 1.3

Figure 1.4

Figure 1.5

Figure 1.6

Figure 1.7

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Figure 1.9

Figure 1.10

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Figure 2.1

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Figure 8.1

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Figure 9.1

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List of Tables

Table 3.1

Table 4.1

Table 4.2

Table 6.1

Table 8.1

Table 8.3

Table 9.1

Table 9.2

Table 9.3

Table 9.4

Table 9.5

Table 9.6



Edited by

Gerard Meijer

Delft University of Technology and SensArt, The Netherlands


Michiel Pertijs

Delft University of Technology, The Netherlands


Kofi Makinwa

Delft University of Technology, The Netherlands






This edition first published 2014

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Library of Congress Cataloging-in-Publication Data

Smart sensor systems : emerging technologies and applications / edited by Gerard C.M. Meijer, Michiel Pertijs,

Kofi Makinwa.

p. cm.

Includes bibliographical references and index.

ISBN 978-0-470-68600-3 (cloth)

1. Detectors– Design and construction. 2. Detectors– Industrial applications. 3. Microcontrollers. I. Meijer,

G. C. M. (Gerard C. M.)

TA165.S55 2008

681′.25– dc22


A catalogue record for this book is available from the British Library.

ISBN: 9780470686003

Cover picture: © Martil Instruments

About the Editors

Gerard Meijer

Gerard Meijer received his M.Sc. and Ph.D. degrees in Electrical Engineering from Delft University of Technology, Delft, The Netherlands, in 1972 and 1982, respectively. Since 1972, he has been a member of the research and teaching staff of Delft University of Technology, and since 2001, an Antoni van Leeuwenhoek Professor. His major interests are in the field of Analog Electronics and Electronic Instrumentation. In 1984, and on a part-time basis from 1985–1987, he was seconded to Delft Instruments Company, Delft, The Netherlands, where he was involved in the development of industrial level gauges and temperature transducers. In 1996 he co-founded the company SENSART, where he is a consultant for the design and development of sensor systems. In 1999 the Dutch Foundation of Technical Sciences STW awarded Meijer with the honorary title of Simon Stevin Meester. Meijer is chairman of the National STW Platform on Sensor Technology.

Michiel Pertijs

Michiel Pertijs received the M.Sc. and Ph.D. degrees in electrical engineering (both cum laude) from Delft University of Technology, Delft, The Netherlands, in 2000 and 2005, respectively. From 2005 to 2008, he was with National Semiconductor, Delft, where he designed precision operational amplifiers and instrumentation amplifiers. From 2008 to 2009, he was a Senior Researcher with imec / Holst Centre, Eindhoven, The Netherlands. In 2009, he joined the Electronic Instrumentation Laboratory of Delft University of Technology, where he is now an Associate Professor. He heads a research group working on integrated circuits for medical ultrasound and energy-efficient smart sensors. Dr. Pertijs is an Associate Editor of the IEEE Journal of Solid-State Circuits (JSSC) and a member of the technical program committees of the International Solid-State Circuits Conference (ISSCC), the European Solid-State Circuits Conference (ESSCIRC), and the IEEE Sensors Conference. He received the ISSCC 2005 Jack Kilby Award for Outstanding Student Paper, the JSSC 2005 Best Paper Award, and the 2006 Simon Stevin Gezel Award from the Dutch Technology Foundation STW.

Kofi Makinwa

Kofi Makinwa received the B.Sc. (First class honors) and M.Sc. degrees from Obafemi Awolowo University, Ile-Ife, Nigeria in 1985 and 1988 respectively. In 1989, he received the M.E.E. degree (cum laude) from the Philips International Institute, Eindhoven, The Netherlands and in 2004, the Ph.D. degree from Delft University of Technology, Delft, The Netherlands. From 1989 to 1999, he was a Research Scientist with Philips Research Laboratories, Eindhoven, The Netherlands, where he worked on interactive displays and on optical and magnetic recording systems. In 1989 he joined the Electronic Instrumentation Laboratory of Delft University of Technology, where he is currently an Antoni van Leeuwenhoek Professor and head of the laboratory. His main interests are in the design of precision analog circuitry, smart sensors and sensor interfaces.

Dr. Makinwa is on the technical program committees of the European Solid-State Circuits Conference (ESSCIRC) and the Advances in Analog Circuit Design (AACD) workshop. He was on the Program Committee of the International Solid-State Circuits Conference (ISSCC) from 2006 to 2012. He has also been a three-time guest editor of the Journal of Solid-State Circuits (JSSC) and a two-term distinguished lecturer of the IEEE Solid-State Circuits Society. He is a co-recipient of the 2005 Simon Stevin Gezel Award from the Dutch Technology Foundation, as well as of several best paper awards: from the JSSC (2005, 2011), the ISSCC (2005, 2008, 2011) and the ESSCIRC (2006, 2009), among others. In 2013, at the 60th anniversary of ISSCC, he was recognized as one of its top ten contributors. He is an IEEE fellow, an alumnus of the Young Academy of the Royal Netherlands Academy of Arts and Sciences and an elected member of the IEEE Solid-State Circuits Society AdCom, the society's governing board.

List of Contributors

Pedram Afshar

, Medtronic Neuromodulation, Minneapolis, USA

Bernhard Boser

, Berkeley Sensor & Actuator Center, University of California, Berkeley, USA

Zu-Yao Chang

, Electronic Instrumentation Laboratory, Delft University of Technology, Delft, The Netherlands

Peng Cong

, Medtronic Neuromodulation, Minneapolis, USA

Tim Denison

, Medtronic Neuromodulation, Minneapolis, USA

Chinwuba Ezekwe

, Robert Bosch, Palo Alto, California, USA

Heidari Ali

, Guilan University, Rasht, Iran

Johan Huijsing

, Electronic Instrumentation Laboratory, Delft University of Technology, Delft, The Netherlands

Blagoy Iliev

, Martil Instruments, Heiloo,The Netherlands

Xiujun Li

, Exalon, Delft, The Netherlands and Sensytech, Delft, The Netherlands

Kofi Makinwa

, Electronic Instrumentation Laboratory, Delft University of Technology, Delft, The Netherlands

Gerard Meijer

, Electronic Instrumentation Laboratory, Delft University of Technology and SensArt, Delft, The Netherlands

Stoyan Nihtianov

, Electronic Instrumentation Laboratory, Delft University of Technology, Delft, The Netherlands

Jos Oudenhoven

, imec/Holst Centre, Eindhoven, The Netherlands

Michiel Pertijs

, Electronic Instrumentation Laboratory, Delft University of Technology, Delft, The Netherlands

Gheorghe Pop

, Martil Instruments, Heiloo, The Netherlands

Valer Pop

, imec/Holst Centre, Eindhoven, The Netherlands

Michael Renaud

, imec/Holst Centre, Eindhoven, The Netherlands

Zhichao Tan

, Electronic Instrumentation Laboratory, Delft University of Technology, Delft, The Netherlands

Albert Theuwissen

, Harvest Imaging, Bree, Belgium and Electronic Instrumentation Laboratory, Delft University of Technology, Delft, The Netherlands

Roland Thewes

, Technische Universität Berlin (Berlin Institute of Technology), Berlin, Germany

Hubregt Visser

, imec/Holst Centre, Eindhoven, The Netherlands

Ruud Vullers

, imec/Holst Centre, Eindhoven, The Netherlands

Ziyang Wang

, imec/Holst Centre, Eindhoven, The Netherlands


This book is intended as a reference for designers and users of sensors and sensor systems, and as a source of inspiration and a trigger for new ideas. For a major part, it is based on material used in the multidisciplinary “Smart Sensor Systems” course, which has been held annually at Delft University of Technology since 1995. The goals of this course are to present the basic principles of smart sensor systems to a broad, multidisciplinary audience, to develop a common language and scientific background to discuss the challenges associated with the design of such systems, and to facilitate mutual cooperation. In this way, we hope to contribute to the continuous expansion of the community of people advancing the exciting field of smart sensor systems.

As diverse and widespread as smart sensors may be today, research and development in this field is far from complete. It is driven by the continuous demand for lower cost, size and power consumption, and for higher performance and greater reliability. Moreover, new sensing principles and technologies are continuously emerging, and so significant effort is required to bring them to maturity. Often, this process involves more than just improving the performance of the transducer concerned. The system around the transducer plays an equally important, if not a more important, role. This system includes the electronics that interface with the transducer, the package that protects the transducer from the environment, and the calibration procedure that ensures that a certain performance specification is met.

This book focuses on these important system aspects, and, in particular, on the design of smart sensor systems, in which sensors and electronics are combined in a single package or even on a single chip to provide improved functionality, performance and reliability. In a previous book entitled “Smart Sensor Systems,” the basics of such systems were covered. This book complements this prior publication by covering a number of emerging sensing technologies and applications, as well as discussing, in more detail, the system aspects of smart sensor design.

The book opens by discussing the exciting possibilities afforded by the combination of sensors and electronics: the accurate processing of small sensor signals (Chapter 1), the adoption of self-calibration techniques (2), and the integration of precision instrumentation amplifiers (3). This is followed by a discussion of a number of sensor systems in which system aspects play a key role: sensing of physical and chemical parameters by means of impedance measurement (4); low-power angular-rate sensing using feedback and background-calibration techniques (5); sensor systems for the detection of biomolecules, such as DNA (6); optical sensor systems-on-a-chip in the form of CMOS image sensors (7); and smart sensors capable of interfacing with the human nervous system (8). Finally, the book also describes emerging technologies for the generation and storage of energy, since these are the key to realizing truly autonomous sensor systems (9).

During the course of writing this text, we have been assisted by many people. We gratefully acknowledge the feedback and suggestions provided by our reviewers: Reinoud Wolffenbuttel of Delft University of Technology, Michael Kraft of the Fraunhofer Institute for Microelectronic Circuits and Systems, Michiel Vellekoop of the University of Bremen, Jan Bosiers of Teledyne DALSA, Firat Yazicioglu of imec, and the authors who also acted as reviewers. At our publisher, John Wiley & Sons, Ltd., we would like to acknowledge the Project Editors Richard Davies, Liz Wingett, and Laura Bell, for their support, encouragement and help in arranging agreements as well as to Production Editor Genna Manaog and Sangeetha Parthasarathy of Laserwords for help throughout the production of this book. Furthermore, we want to express our gratitude to the universities, research institutes and companies who permitted the use of figures and illustrations to make this book attractive for our readers. Finally, we would like to thank our spouses, Rumiana, Hannah and Abi, for their love and support.

Gerard Meijer, Michiel Pertijs and Kofi MakinwaDelft, The Netherlands

Chapter 1Smart Sensor Design

Kofi Makinwa

Electronic Instrumentation Laboratory, Delft University of Technology, Delft, The Netherlands

This chapter is an expanded and updated version of [7].

1.1 Introduction

Sensors have become a ubiquitous part of today's world. Modern cars employ tens of sensors, ranging from simple position sensors to multi-axis MEMS accelerometers and gyroscopes. These sensors enhance engine performance and reliability, ensure compliance with environmental standards, and increase occupant comfort and safety. In another example, modern homes contain several sensors, ranging from simple thermostats to infrared motion sensors and thermal gas flow sensors. However, the best example of the ubiquity of sensors is probably the mobile phone, which has evolved from a simple communications device into a veritable sensor platform. A modern mobile phone will typically contain several sensors: a touch sensor, a microphone, one or two image sensors, inertial sensors, magnetic sensors, and environmental sensors for temperature, pressure and even humidity. Together with a GPS receiver for position location, these sensors greatly enhance ease of use and have extended the utility of mobile phones far beyond their original role as portable telephones.

Today, most of the sensors in a mobile phone, as well as most sensors intended for consumer applications, are made from silicon. This is mainly because silicon sensors can be mass-produced at low cost by exploiting the large manufacturing base established by the semiconductor industry. Another important motivation is the fact that the electronic circuitry required to bias a sensor and condition its output can be readily realized on the same substrate or, at least, in the same package. It also helps that semiconductor-grade silicon is a highly pure material with well-defined physical properties, some of which can be tuned by doping, and which can be precisely machined at the nanometer scale.

Silicon is a versatile material, one that exhibits a wide range of physical phenomena and so can be used to realize many different kinds of sensors [1]. For example, magnetic fields can be sensed via the Hall effect, temperature differences can be sensed via the Seebeck effect, mechanical strain can be sensed via the piezo-resistive effect and light can be sensed via the photo-electric effect. In addition, measurands that do not directly interact with silicon can often be indirectly sensed with the help of silicon-compatible materials. For example, humidity can be sensed by measuring the dielectric constant of a hygroscopic polymer [2], while gas concentration can be sensed by measuring the resistance of a suitably adsorbing metal oxide [3]. It should be noted that although silicon sensors may not achieve best-in-class performance, their utility and increasing popularity stems from their small size, low cost and the ease-of-use conferred by their co-integrated electronic circuitry.

Sensors are most useful when they are part of a larger system that is capable of processing and acting upon the information that they provide. This information must therefore be transmitted to the rest of the system in a robust and standardized manner. However, since sensors typically output weak analog signals, this task must be performed by additional electronic circuitry. Such interface electronics is best located close to the sensor, to minimize interference and avoid transmission losses. When they are both located in the same package, the combination of sensor and interface electronics is what we shall refer to as a smart sensor [4].

In addition to providing a robust signal to the outside world, the interface electronics of a smart sensor can be used to perform traditional signal processing functions such as filtering, linearization and compression. But it can also be used to increase the sensor's reliability by implementing self-test and even self-calibration functionality (as will be discussed in Chapter 2). A recent trend is towards sensor fusion, in which the outputs of multiple sensors in a package are combined to generate a more reliable output. For example, the outputs of gyroscopes, accelerometers and magnetic sensors can be combined to obtain robust position estimates, thus enabling mobile devices with indoor navigational capability.

This chapter discusses the design of smart sensor systems, in general, and the design of smart sensors in standard integrated circuit (CMOS) technology, in particular. Examples will be given of the design of state-of-the-art CMOS smart sensors for the measurement of temperature, wind velocity and magnetic field. Although the use of standard CMOS technology constrains the performance of the actual sensors, it minimizes cost, and as will be shown, the performance of the overall sensor system can often be significantly improved with the help of the co-integrated interface electronics.

1.2 Smart Sensors

A smart sensor is a system-in-package in which a sensor and dedicated interface electronics are realized. It may consist of a single chip, as is the case with smart temperature sensors, image sensors and magnetic field sensors. However, in cases when the sensor cannot be implemented in the same technology as the interface electronics, a two-chip solution is required. Since this also decouples the production yield of the circuit from that of the sensor, a two-chip solution is often more cost effective, even in cases where the sensor could be co-integrated with the electronics. Examples of two-chip sensors are mechanical sensors, such as MEMS accelerometers, gyroscopes and microphones, whose manufacture requires the use of micro-machining technology.

Since silicon chips, and especially their connections to the outside world, are rather fragile, smart sensors must be protected by some kind of packaging. The design of an appropriate package can be quite challenging since it must satisfy two conflicting requirements: allowing the sensor to interact with the measurand, while protecting it (and its interface electronics) from environmental damage. In the case of temperature and magnetic sensors, more or less standard integrated circuit packages can be employed. Standard packaging can also be used for inertial sensors, provided that a capping die or layer is used to protect their moving parts. In general, however, most sensors require custom packaging, which significantly increases their cost and usually involves a compromise between performance and robustness.

As has been noted earlier, silicon sensors are not necessarily best-in-class. However, the co-integrated interface electronics can be used to improve the performance of the overall system, either by operating the sensor in an optimal mode or by compensating for some of its non-idealities. This requires a good knowledge of the sensor's characteristics. For example, electronic circuitry can be used to incorporate MEMS inertial sensors in an electro-mechanical feedback loop, which, in general, results in improved linearity and wider bandwidth [5]. An example of such a system will be presented in Chapter 5, which describes the use of feedback and compensation circuits to enhance the performance of a MEMS gyroscope. Knowledge of the sensor's characteristics is also necessary to compensate for its cross-sensitivities, for example, to ambient temperature and packaging stress. The design of a smart sensor thus involves the optimization of an entire system and is, therefore, an exercise in system design.

1.2.1 Interface Electronics

To communicate with the outside world, the output of a smart sensor should preferably be a digital signal, although duty-cycle or frequency modulated signals are also microprocessor-compatible and so are sometimes used. The current trend in smart sensor design is to digitize the sensor's output as early as possible, and then to perform any additional signal conditioning, such as filtering, linearization, cross-sensitivity compensation and so on, in the digital domain. This approach facilitates the interconnection of several sensors via a digital bus, and takes advantage of the flexibility and ever-increasing digital signal processing capability of integrated circuitry. A similar trend can be observed in radio receivers, whose ADCs are moving closer and closer to the antenna, and which are thus employing more and more digital signal processing [6].

However, most sensors output low-level analog signals. This is especially true of silicon sensors such as thermopiles, Hall plates and piezo-resistive strain gauges, whose outputs contain information at the microvolt level. One reason for this is the nature of the transduction mechanisms available in silicon. Another is that their small size limits the amount of energy that they can extract from their environment. While this is a desirable feature in a sensor, which should not disturb, that is, extract energy from, the physical process that it observes, it makes the design of transparent interface electronics quite challenging. Great care must be taken to ensure that circuit non-idealities, such as thermal noise and offset, do not limit the performance of a smart sensor.

A further design challenge arises from the fact that the signal bandwidth of most sensors includes DC. As a result, the design of transparent interface electronics, especially in today's mainstream CMOS technology, involves a constant battle against random error sources such as drift and noise, as well as against systematic errors caused by component mismatch, charge injection and leakage currents.

Fortunately, most sensors are quite slow compared to the switching speed of transistors, and so dynamic error correction techniques, which essentially trade speed or bandwidth for precision, can be used to correct for systematic errors [7]. As the term “dynamic” implies, these techniques act continuously to reduce such errors, and so also mitigate the effects of low-frequency random errors due to drift and noise. In general, dynamic error correction techniques can be divided into two categories: sample-and-correct techniques and modulate-and-filter techniques.

An example of a sample-and-correct technique is auto-zeroing (Figure 1.1), in which the input of an amplifier is periodically shorted, while its output is fed to an offset-canceling integrator [8]. During normal operation the integrator's input is disconnected, thus freezing its output and canceling the amplifier's instantaneous offset (and noise). The main drawback of auto-zeroing is that the need to short-circuit the amplifier's input reduces its availability. However, this can be circumvented by using two, alternately auto-zeroed, amplifiers in a so-called ping-pong configuration [9].

Figure 1.1 Simplified block diagram of an auto-zeroed amplifier

An alternative way to reduce amplifier offset is known as chopping, and it is an example of a modulate-and-filter technique. The input signal is modulated by a square-wave, amplified and then demodulated [8]. As shown in Figure 1.2, this sequence of operations modulates the amplifier's offset (and noise) to the chopping frequency which facilitates their removal by a low-pass (averaging) filter. However, the filter also limits the amplifier's useful bandwidth. This drawback can be circumvented by the use of chopper-stabilized amplifiers in which a chopper amplifier is used to improve the low frequency characteristics of a wide-band main amplifier [10]. The design of precision amplifiers based on various combinations of chopping and auto-zeroing will be discussed in more detail in Chapter 3.

Figure 1.2 Simplified diagram of a chopper amplifier

High-resolution analog-to-digital conversion can be achieved by employing a technique known as sigma-delta (or delta-sigma) modulation, in which a low-pass filter, an ADC and a DAC are combined to form a feedback loop [11]. As shown in Figure 1.3, the ADC's quantization error (which can be usefully modeled as random noise) will then be high-pass filtered when it is referred to the input of the loop. This noise-shaping property of the loop allows a sigma-delta modulator to achieve very high resolution in a narrow bandwidth. The quantization noise outside this bandwidth can then be removed by a succeeding digital low-pass filter (not shown in Figure 1.3). By combining various dynamic error correction techniques with sigma-delta modulation, ADCs with more than 20-bit resolution and 18-bit linearity have been realized [12], [13].

Figure 1.3 Simplified diagram of a sigma-delta modulator

1.2.2 Calibration and Trimming

Like all sensors, the accuracy of a smart sensor can only be evaluated by calibrating it against a known standard, after which its systematic inaccuracy is known. This can then be reduced in a subsequent trimming operation. The main limitation on the sensor's accuracy then becomes its stability over time. Trimming is a powerful technique, which can be used to correct for many errors resulting from manufacturing tolerances and process spread. However, in sensors intended for high-volume production, it should be seen as a method of last resort, since the associated calibration requires extra test equipment and takes up costly production time. These topics will be discussed in more detail in the following chapter.

1.3 A Smart Temperature Sensor

In this section, the design of a high-accuracy temperature sensor in standard CMOS will be described [14]. The sensing element is the substrate bipolar junction transistor that is available in all CMOS processes. However, it is a parasitic device, whose characteristics exhibit significant process spread. As a result, the resulting temperature sensor must be trimmed in order to achieve inaccuracies less than

1.3.1 Operating Principle

The base-emitter voltage of a bipolar junction transistor is given by:


where is its collector current and is a process-dependent parameter which also depends on the transistor's size. As shown in Figure 1.4, is a near-linear function of temperature with a slope of approximately A voltage that is proportional-to-absolute-temperature (PTAT) can then be obtained by measuring the difference between the base-emitter voltages of two, nominally identical, bipolar junction transistors biased at a 1 : p current ratio:


Figure 1.4 Simplified circuit diagram of the CMOS smart temperature sensor

If the current ratio is well defined, will be an accurate function of absolute temperature, since it does not depend on or any other process-dependent parameters. However, it is a small signal, with a sensitivity of about 140 µV/K (for ), which means that low-offset interface electronics is required.

In order to digitize a reference voltage is also required. As shown in Figures 1.4 and 1.5, a so-called band-gap reference voltage can be obtained by combining with a scaled version of Both voltages can then be applied to an ADC, which determines their temperature-dependent ratio


Figure 1.5 Temperature dependence of the voltages generated by the circuit in Figure 1.4; the shaded areas indicate the effect of process spread

Assuming that the interface electronics is ideal, the sensor's main source of error will be the effect of process spread on As discussed in [4] in Chapter 7, [16] and shown in Figure 1.5, this only affects the slope of while the extrapolated value of at 0 K, known as remains the same. This means that the effect of process spread can be corrected for by calibrating the sensor at room temperature and then adding a PTAT correction voltage to for instance by trimming the current in the Figure 1.4 circuit. This will be discussed in more detail in Chapter 2.

1.3.2 Interface Electronics

A simplified block diagram of the sensor's interface electronics is shown in Figure 1.6. It is based on a second-order single-bit sigma-delta modulator, which converts and into a temperature-dependent bitstream The modulator employs a charge-balancing scheme in which its input is either or depending on the instantaneous value of the bitstream. It can be shown [14], [16], that the resulting bitstream average is exactly equal to as given by (3). So in contrast to the scheme shown in Figure 1.4, an explicit reference voltage does not need to be generated, which simplifies the required circuitry. The scale factor is established by appropriately sizing the sampling capacitors at the input of the modulator.

Figure 1.6 Simplified circuit diagram of the CMOS smart temperature sensor

To achieve the targeted inaccuracy of the errors introduced by the interface electronics should all be reduced to the level. This means, for instance, that the modulator's offset should be less than 2 µV, while the bias current ratio and the scale factor should be accurate to within about 100 ppm. Dynamic error correction techniques were used to achieve this level of accuracy, since the manufacturing tolerance of a typical CMOS process mean that the best-case component mismatch is only about 0.1%.

Figure 1.6 also shows how a technique known as dynamic element matching (DEM) was used to obtain an accurate 1:5 bias current ratio. Via a set of switches, one of six nominally equal current sources is connected to while the others are connected to This leads to six possible connections, each of which may result in an inaccurate due to the mismatch between the current sources. The average value of however, is much more accurate, because the mismatch errors cancel out 4, Chapter 7; [15]. The required averaging can conveniently be performed by the same digital filter that suppresses the sigma-delta modulator's quantization noise. A similar DEM scheme was used to average out errors due to mismatch in the sampling capacitors of the modulator.

The modulator's input-referred offset was reduced by the use of correlated double sampling (a technique very similar to auto-zeroing) in the first integrator [8], [17]. Since this did not reduce the offset sufficiently, the entire modulator was also chopped, which ensures that its residual offset is well below the 2 µV level.

As shown in Figure 1.6, the sensor was trimmed by adjusting the bias current of transistor with the help of a 10-bit current DAC, consisting of a digital first-order sigma-delta modulator whose output modulates one of the bias current sources. The DAC covers the expected trimming range with a resolution of Since the bitstream output of the DAC may interfere with the bitstream output of the main modulator, the timing of the DAC, as well as that of the other DEM schemes, was synchronized to the main modulator's bit-stream [18].

The resulting sensor consumes 190 µW and achieves an inaccuracy of over the military temperature range ( to ) after a single room-temperature trim. This level of accuracy still represents the state-of-the-art for CMOS temperature sensors [19].

1.3.3 Recent Work

Recent work has focused on simplifying the sensor's calibration, as well as reducing its power dissipation. Calibrating a temperature sensor with the help of a reference sensor can be a time-consuming and hence expensive process, since achieving the necessary thermal equilibrium between the sensors can take several minutes. By regarding as a sufficiently accurate measure of temperature, and then digitizing it with respect to an accurate external voltage reference, the sensor can be voltage-calibrated in less than a second with an inaccuracy of less than [20]. Increasing the sensor's efficiency was achieved by using a more efficient two-step ADC that combines a coarse step, based on binary search, followed by a fine step, based on sigma-delta modulation [21]. Over the military temperature range ( to ), the resulting sensor achieves an inaccuracy of after voltage calibration, which is only slightly less than the state-of-the-art. However, it dissipates only 5 µW, which is nearly 40x less than its predecessor [14].

1.4 A Smart Wind Sensor

In this section, the design of a smart wind sensor is described, that is, a solid-state sensor that measures wind speed and direction with no moving parts [22]. The sensor makes use of the fact that wind passing above a heated object will cool it asymmetrically. Wind speed and direction can then be determined by measuring the resulting temperature gradient. If the object is a chip, it can be heated by passing current through resistors, while the wind-induced temperature gradient can be sensed by integrated thermopiles.

1.4.1 Operating Principle

As shown in Figure 1.7, the flow of air over a heated disc will cool it non-uniformly. The result is a temperature gradient between any two points located symmetrically around the center of the disk. The magnitude of is proportional to the square-root of flow speed, while its direction is aligned with that of the flow. Thus, by measuring both wind speed and direction can be determined [23], [24].

Figure 1.7 Operating principle of the wind sensor

Although heaters and temperature sensors can be readily integrated on a standard CMOS chip, the requirement that it must sense a flow-induced temperature gradient precludes the use of standard packages. Instead, as shown in Figure 1.7, the chip is glued to the underside of a thin ceramic disc, while the airflow is passed over the other side. This simple and robust packaging solution ensures that the chip is in good thermal contact with the flow. The disc is then mounted in an aerodynamic housing, which ensures that the wind sensor is only sensitive to the horizontal components of the wind [23].

As shown in Figure 1.8, four heaters and four thermopiles are realized on the sensor chip. The thermopiles are configured to measure orthogonal components of the flow-induced temperature gradient. Since silicon is a good thermal conductor, these are quite small: in the order of a few tenths of a degree. As a result, the output of the thermopiles is at the microvolt level. In a first-generation sensor, these signals were digitized by precision off-chip electronics, and the results used to compute wind speed and direction. The resulting errors in the computed wind speed and direction are typically less than 5% and respectively [25].

Figure 1.8 Schematic layout of the wind sensor

Due to manufacturing tolerances, the assembled wind sensor must be calibrated and trimmed. This is because, in general, the chip will not be located exactly in the center of the disc, and so the hot-spot on the disc may not be centered on the chip. The result is a flow-dependent thermal offset, which can be much larger than the actual flow-induced temperature differences. This offset can be cancelled by trimming the power dissipated in the four heaters, so as to center the heat distribution on the chip [26]. The sensor is then calibrated in a wind tunnel. The resulting data is stored in a non-volatile memory, and used to compensate for the effects of any residual offset and gain errors. However, the whole procedure is time consuming and adds significantly to the sensor's cost.

To circumvent these problems, the smart wind sensor was operated in an alternative mode: the temperature-balance mode [27], [28]. In this mode, the flow-induced temperature gradient is continuously canceled by dynamically adjusting the power dissipated in the heaters. This automatically centers the heat distribution on the chip, and as a result, any thermal offset becomes a well-defined function of flow speed. Moreover, the heater power does not need to be manually trimmed. Flow speed and direction can then be computed from the differential heat power required to cancel each component of the flow-induced temperature gradient [29]. This approach also simplifies the interface electronics, which now has the much easier task of digitizing the relatively large signals (several tens of milliwatts) applied to the heaters instead of the microvolt-level outputs of the thermopiles.

1.4.2 Interface Electronics

The block diagram of the smart wind-sensor chip is shown in Figure 1.9. It consists of three thermal sigma-delta modulators, two of which are arranged to cancel the north-south and east-west components of any on-chip temperature gradient [22]. Their bitstream outputs are then a digital representation of the differential heating powers and required to cancel the two components and respectively. The heat pulses generated by the modulators are low-pass filtered by the sensor's thermal capacitance, and thus the sensor itself functions as the modulators' loop filter. So in addition to the flow sensor, each modulator only requires the implementation of a clocked comparator, which leads to a very compact architecture. Since the thermopile's output is at the microvolt level, the comparator was auto-zeroed to reduce its offset [22], [29].

Figure 1.9 Block diagram of the smart wind sensor

A third thermal sigma-delta modulator maintains the sensor at a constant temperature (the overheat ) above ambient temperature. In this mode, the magnitude of will be proportional to the square-root of wind speed [23]. The temperature of the chip is measured by a substrate PNP transistor at the center of the chip, while an external transistor measures ambient temperature (Figure 1.9). As in the smart temperature sensor, these transistors are biased at two different collector currents, in order to generate a voltage proportional to the overheat. By using an auto-zeroed comparator and well-matched current sources, the error in the overheat due to process spread will be limited to about Although this error will alter the sensor's sensitivity, its effect is also taken into account by the sensor's calibration.

After calibration, which will be discussed in more detail in Chapter 2, the smart sensor was tested in a wind tunnel at wind speeds ranging from 1 m/s to 25 m/s. The errors in the computed speed and direction were less than 4% and 2°, which are slightly less than those of an earlier wind sensor without on-chip interface electronics [23–25]. Due to the compact interface architecture, this was achieved with no increase in chip area.

1.4.3 Recent Work

Recent work has focused on simplifying the sensor's construction and reducing its power dissipation. In [30], the sensor was operated in a so-called constant power mode, in which its overheat was not regulated, thus eliminating the need for the external temperature sensor and the associated overheat control loop. As a result, the heater power can be drastically reduced, since no guard-band needs to be maintained to accommodate errors in the overheat control loop. To maintain resolution at such decreased heater power levels, the in-band quantization noise of the thermal sigma-delta modulators was reduced by connecting an electrical filter (an integrator) in series with the sensor's thermal filter. The lower bound on heater power dissipation was then found to be set by the integrator's residual offset. In [31], this was reduced by the application of system-level chopping. The resulting wind sensor dissipates only 25 mW, 16x less than that of [22], while achieving the same accuracy after calibration, that is, less than 4% and 2° error in wind speed and direction, respectively.

1.5 A Smart Hall Sensor

In this section, the design of a smart magnetic field sensor intended for compass applications is described [32]. The sensor is based on the Hall effect, that is, the fact that if a plate carrying current along one axis is exposed to a magnetic field, a voltage will be induced across its transverse axis. The magnitude of this Hall voltage is proportional to the current through the plate and to the normal component of the magnetic field 4, Chapter 9; [33].

1.5.1 Operating Principle

In a CMOS process, a Hall plate will usually consist of an n-well layer, resulting in a sensitivity of about 100 V/AT. At a typical bias current of 1 mA, the earth's 50 µT (max) magnetic field will then result in a Hall voltage of only 5 µV. Accurately digitizing such a small voltage presents a significant interfacing challenge.

Furthermore, silicon Hall plates exhibit considerable offset (5 mT to 50 mT) due to mechanical stress, doping variations and lithographic errors. Although this is much larger than the earth's magnetic field, this would not be a major problem in itself since magnetic compasses are usually calibrated to compensate for the presence of nearby ferromagnetic materials. The main problem in a compass application is offset drift, which will result in a time-varying angular error.

The offset of a Hall plate can be reduced to the 10 µT level by the spinning current method; in which the Hall plate's bias current is spatially rotated while its output is averaged in time [34]. This also reduces drift, but not sufficiently for a compass application, especially in the presence of the mechanical stress caused by low-cost plastic packaging. Offset and drift can also be reduced by orthogonally coupling two or more Hall plates [23]. The smart sensor described here uses the spinning current technique and four orthogonally-coupled Hall plates to achieve the smallest possible offset and drift.

Packaging presents another challenge, since standard IC packages typically contain trace amounts of ferromagnetic materials, which may distort the magnetic field around the sensor. To avoid such errors, a custom package which is free of ferromagnetic materials has been developed [35]. It is designed so that an electronic compass can be made by mounting three sensors both vertically (as shown in Figure 1.10) and horizontally on a PCB.

Figure 1.10 Custom-packaged smart Hall sensor

1.5.2 Interface Electronics

The block diagram of the sensor's interface electronics is shown in Figure 1.11. It consists of a voltage-to-current converter (VIC), whose output is digitized by a first-order sigma-delta modulator. The output of the modulator is averaged over an entire spinning-current cycle by an up/down counter, and the result is transmitted to the outside world via a compatible serial interface.

Figure 1.11 Block diagram of the smart Hall sensor

Due to their offset, the output of the four Hall plates can be as high as 50 mV during the various phases of a spinning cycle. The average value, however, is much smaller and is less than 50 nV under zero field conditions. The interface electronics should, therefore, have an input-referred offset of less than 50 nV and a linear dynamic range of about 120 dB, which is quite challenging.

To achieve this level of linearity, the VIC consists of two opamps (Figure 1.12), each with a DC gain of over 120 dB, which generate its output current by applying the output voltage of the Hall plates across a resistor. A so-called nested chopping scheme was used to reduce its offset to the desired 50 nV level [36]. As shown in Figure 1.9, the VIC is first chopped by a pair of “fast” choppers driven by the 12.5 kHz ClkChop signal. The residual offset (due to spikes associated with the operation of the input choppers) was further reduced by creating a dead-band [37]. This was implemented via the EnCM signal (a 1 ms pulse), which connects the VIC output to a reference voltage CMref after every ClkChop transition, while simultaneously opening the output switches. To reduce the input-referred offset even further, the entire front-end is chopped at about 10 Hz by periodically inverting the polarity of the Hall plate's bias current and simultaneously inverting the sign of the modulator's bitstream.

Figure 1.12 Schematic diagram of the chopped voltage-to-current converter

The result is a sensor with an offset of 4 µT, an offset temperature coefficient of only 8 nT/K and an offset drift of less than 0.25 µT even after aggressive thermal cycling [38]. In a compass application, this offset drift corresponds to an angular error of less then To date, this represents the best offset performance reported for a CMOS Hall sensor.

1.5.3 Recent Work

Standard (horizontal) Hall sensors are only sensitive to magnetic fields normal to the chip's surface. A 3D magnetic compass then requires three orthogonally-oriented chips. An alternative is to combine horizontal and so-called vertical Hall plates on a single chip [40], but the latter have much higher offset and so are not suited for compass applications. Recently, single-chip 3D sensors based on thin-film integrated magnetic concentrators have been developed. These concentrators bend in-plane magnetic field components towards the perpendicular where they can be sensed by horizontal Hall sensors [41], [42].

Another recent development is the co-integration of auxiliary stress and temperature sensors with Hall sensors. The information provided by these auxiliary sensors can then be used to compensate for the cross-sensitivity of the Hall sensors to temperature changes and packaging stress 4, Chapter 9; [43], [44].

1.6 Conclusions

The designs described above show that, at least for integrated temperature, flow and magnetic sensors, it is possible to design transparent interface electronics in standard CMOS. Compared to electronic circuits, most sensors are quite slow, which means that the effects of typical circuit non-idealities such as offset, gain error and noise, can be reduced to negligible levels by dynamic error correction techniques such as auto-zeroing, chopping, DEM, switched-capacitor filtering and sigma-delta modulation.

For example, by using various combinations of auto-zeroing and chopping, amplifiers with input-referred offsets of less than 100 nV can be realized, which, for input levels of a few volts, corresponds to a 24-bit DC dynamic range. Also, by using DEM, current and voltage ratios, that is, gain factors, can be defined to better than 100 ppm accuracy. Finally, ADCs based on sigma-delta modulators can be used to flexibly trade-off resolution for bandwidth, and are capable of achieving up to 22-bit resolution in bandwidths of a few tens of Hertz. As an added bonus, the notches in the frequency response of their decimation filters can be used to completely suppress the AC residuals produced by chopping and DEM.

So what can we do with all this precision? It can be used to realize novel sensors based on transduction mechanisms that result in very small, and previously undetectable, signals. One example is the implementation of temperature sensors based on the well-defined thermal diffusivity of bulk silicon, which require the detection of the small temperature variations created by the diffusion of heat pulses through a chip [39]. In existing smart sensors, precision can be traded off against other performance criteria, such as chip area and power dissipation. For example, since DEM mitigates the effects of component mismatch, larger initial mismatch can be tolerated, which means that smaller components can be used. Similarly, since chopping suppresses noise, a given signal-to-noise ratio can be obtained at lower power consumption.

The design of smart sensor systems involves meeting the engineering challenge associated with the design of accurate, reliable systems using inaccurate, low-cost components. Due to the wide variety of sensing principles, packaging methods and circuit techniques that can be used to realize such systems, their design is more of an art than a science. The dynamic techniques described above have been shown to be of great value in meeting this challenge, and will undoubtedly continue to be of use as we further master the art of designing smart sensor systems.


[1] S. Middelhoek and S. Audet,

Silicon Sensors

, London: Academic Press, 1989.

[2] Z. Tan, R. Daamen, A. Humbert, Y.V. Ponomarev, Y. Chae, and M.A.P. Pertijs, “A 1.2-V 8.3-nJ CMOS humidity sensor for RFID applications,”

Journal of Solid-State Circuits

, vol.


, no. 10, pp. 2469–2477, October 2013.

[3] M. Graf, U. Frey, S. Taschini, and A. Hierlemann, “Micro hot plate-based sensor array system for the detection of environmentally relevant gases,”

Analytical Chemistry

, vol.


, no. 19, pp. 6801–6808, 2006.

[4] G.C.M. Meijer, Ed.

Smart Sensor Systems

. John Wiley & Sons Ltd, 2008.

[5] N. Yazdi, F. Ayazi, and K. Najafi, “Micromachined inertial sensors,” Proc. IEEE, vol.


, no. 8, pp. 1640–1659, August 1998.

[6] P.G.R. Costa, L.J. Breems, K.A.A. Makinwa, R. Roovers, and J.H. Huijsing “A 118 dB dynamic range continuous-time IF-to-baseband sigma-delta modulator for AM/FM/IBOC radio receivers,”

Journal of Solid-State Circuits

, vol.


, pp. 1076–1089, May 2007.

[7] K.A.A. Makinwa, M.A.P. Pertijs, J.C. van der Meer, and J.H. Huijsing, “Smart sensor design: the art of compensation and cancellation,”




, pp. 76–82, September 2007.

[8] C.C. Enz and G.C. Temes, “Circuit techniques for reducing the effects of op-amp imperfections: autozeroing, correlated double sampling and chopper stabilization,” Proc. IEEE, vol.


, no. 11, 1584–1614, November 1996.

[9] C.G. Yu and Randall L. Geiger. “An automatic offset compensation scheme with ping-pong control for CMOS operational amplifiers.”

Journal of Solid-State Circuits

, vol.


, no. 5, pp. 601–610, May 1994.

[10] Q. Fan, J.H. Huijsing, and K.A.A. Makinwa, “A 21 nV/√Hz chopper-stabilized multipath current-feedback instrumentation amplifier with 2 µV offset,”

Journal of Solid-State Circuits

, vol.


, no. 2, pp. 464–475, February 2012.

[11] R. Schreier and G.C. Temes.

Understanding Delta-sigma Data Converters

. vol.


. Piscataway, NJ: IEEE Press, 2005.

[12] V. Quiquempoix, P. Deval, A. Barreto, G. Bellini, J. Markus, J. Silva, and G. Temes, “A low-power 22-bit incremental ADC,”

Journal of Solid-State Circuits

, vol.


, pp. 1562–1571, July 2006.

[13] Y.C. Chae, K. Souri, and K.A.A. Makinwa, “A 6.3 µW 20bit incremental zoom-ADC with 6 ppm INL and 1 µV offset,”

Journal of Solid-State Circuits

, vol.


, pp. 3019–3027, December 2013.

[14] M.A.P. Pertijs, K.A.A. Makinwa, and J.H. Huijsing, “A CMOS temperature sensor with a 3σ inaccuracy of ±0.1°C from −55°C to 125°C,”

Journal of Solid-State Circuits

, vol.


, pp. 2805–2815, December 2005.

[15] G.C.M. Meijer, G. Wang, and F. Fruett, “Temperature sensors and voltage references implemented in CMOS technology,”

IEEE Sensors Journal

, vol.


, no. 3, pp. 225–234, October 2001.

[16] M.A.P. Pertijs and J.H. Huijsing,

Precision Temperature Sensors in CMOS Technology

. Dordrecht, The Netherlands: Springer, 2006.

[17] C. Hagleitner, D. Lange, A. Hierlemann, O. Brand, and H. Baltes, “CMOS single-chip gas detection system comprising capacitive, calorimetric and mass-sensitive microsensors,”

IEEE Journal of Solid-State Circuits

, vol.


, pp. 1867–1878, 2002.