CMOS interface circuits for metal-oxide gas sensors

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CMOS interface circuits for

metal-oxide gas sensors

by

Luis Carlos ´

Alvarez Sim´on

A dissertation submitted to the Electronics

Department in partial fulfillment of the

requirements for the degree of

D.Sc. in Electronics

at the

National Institute for Astrophysics, Optics

and Electronics

December 2014

Tonantzintla, Puebla

Advisor:

Dra. Mar´ıa Teresa Sanz Pascual

c

INAOE 2014

All rights reserved

The author hereby grants to INAOE permission

to reproduce and to distribute copies of this

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CMOS interface circuits for

metal-oxide gas sensors

by

M.C. Luis Carlos Álvarez Simón

Supervised by:

Dra. María Teresa Sanz Pascual

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Abstract

The development of portable, low cost, low power and high performance gas

sensing systems based on MOX gas sensors involves advances in three areas: chem-ical sensors, data analysis algorithms or information extraction methods and elec-tronic circuits to interface the sensors with the data processor. Currently, several data analysis algorithms and operation modes of MOX gas sensors have been developed and are being tested to improve their poor selectivity and stability of such sensors. However, to attain a practical high accuracy gas sensing system, it is also necessary to develop a high accuracy interface circuit. Furthermore, for sensor network applications, the interface circuit needs to have low power consumption and low cost.

This thesis proposes circuit topologies to implement CMOS interface circuits for resistive chemical sensors. Specifically a general topology is proposed for the read-out circuit that not only handles a wide dynamic range of resistance variation but also allows to implement resistance to period converters robust to supply and environment temperature variations. Furthermore, a flexible temperature control system is presented that enables handling heaters with different requirements in power consumption to achieve the desired operating temperature of the sensor. The simplicity and flexibility of the proposed topologies can facilitate the interface circuit implementation to different technology nodes.

The proposed readouts and temperature control circuits were implemented in an 180nm CMOS technology. One the one hand, five Resistance-to-Period Converters (RTC) based on first order relaxation oscillators were designed. The circuits were tested with_±10%of variations in supply voltage and a range of environment

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iv

perature from 3 to 95◦

C. Experimental results showed that the proposed topology can get robust RTCs to temperature and supply voltage variations without accu-racy biasing circuits or high-performance sub-circuits, maintaining deviations in the output signal in the order of _±1% in almost two decades of resistance varia-tions. On the other hand, a temperature control circuit based on the on/off control technique was also designed. The functionality of the control circuit was tested using the commercial AS-MLC gas sensor. Experimental results showed a resolu-tion of _±4.5◦

C in the operating temperature control. All of the aforementioned adds up to reliable interface circuits that can be used to for the development of portable and low-cost gas sensing system.

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Resumen

El desarrollo de sistemas de sensado de gas portables de bajo costo, baja potencia y alto desempeño basados en sensores de gas de óxido-metálico involucra el avance de tres áreas: sensores químicos, algoritmos para el análisis de datos o métodos de extracción de información y circuitos de interface para interconectar los sensores con el procesador de datos. Actualmente, se han desarrollado diversos algoritmos para el análisis de datos y modos de operación de los sensores químico-resistivos, esto con el objetivo de mejorar la selectividad y estabilidad de dichos sensores. Sin embargo, para obtener un sistema de sensado de alta exactitud es necesario también el desarrollo de un circuito de interface de alta exactitud. Además, para aplicaciones en redes de sensores, el circuito de interface deberá ser de bajo costo y bajo consumo de potencia.

En esta tesis se proponen topologías de circuitos para el acondicionamiento de señal de sensores de gas químico-resistivos. En especial se propone una topología general que no solo permite obtener un rango dinámico mayor a dos décadas, característica necesaria para manejar este tipo de sensores, sino que también per-mite implementar convertidores de resistencia a periodo robustos a variaciones del voltaje de alimentación y la temperatura ambiente. Además de los conver-tidores de resistencia a frecuencia se propone también un circuito de control de temperatura que permite manejar sensores de gas con diferentes requerimientos en cuanto al consumo de potencia para llegar a la temperatura de operación deseada de la película sensible. La simplicidad y flexibilidad de las topologías propuestas facilitan su migración a diferentes nodos de tecnología.

Los circuitos de interfaz propuestos fueron implementados en una tecnología CMOS

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vi

de 180nm. Por una parte, se diseñaron cinco convertidores de Resistencia a Pe-riodo (RTC) basados en un oscilador de primer orden. Todos los circuitos fueron puestos a prueba a variaciones del voltaje de_±10% y variaciones de la temperatura ambiente de 3 a 95◦

C. Con los resultados experimentales obtenidos se demostró que la topología propuesta permite obtener RTCs robustos a variaciones de tem-peratura y del voltaje de alimentación, cabe destacar que la robustez se consigue sin la necesidad de usar circuitos de polarización precisas ni sub-circuitos de alto desempeño. Bajo dichas pruebas, las variaciones del periodo de la señal de sal-ida se mantuvieron por debajo de _±1% en al menos dos décadas de variación de la resistencia. Por otra parte, se diseñó tambien un circuito de control de tem-peratura de tipo on/off, cuya funcionalidad fué probada empleando el sensor de gas comercial AS-MLC. Los resultados experimentales mostraron el control de la temperatura de operación del sensor con una resolución de_±4.5◦

C. Las ventajas que proporcionan las topologías propuestas pueden ser usados para el desarrollo de sistemas de sensado de gas portables de bajo costo.

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Agradecimientos

Agradezco enormemente a la Dra. María Teresa Sanz Pascual, no solo por ofre-cerme sus valiosos conocimientos y experiencia profesional en la dirección de este trabajo, sino también por su paciencia, su apoyo y ánimo en cada situación que surgía durante el desarrollo de la tesis.

Quisiera agradecer de una manera muy especial a los Dres. Santiago Celma y Nicolás Medrano de la Universidad de Zaragoza, España, por su inestimable ayuda durante la estancia que realicé en dicha universidad. Así como también, a todo el grupo de diseño electrónico quienes siempre estuvieron dispuestos a apoyar.

Agradezco al CONACYT por la beca otorgada para la realización de mis estudios, ya que sin ella no me hubiera sido posible realizar este trabajo.

A todos los investigadores que fueron parte de mi formación académica, así como también a todo el personal administrativo, técnico y de mantenimiento, ya que cada uno de ellos forma parte del sistema que hizo posible la culminación de esta tesis.

Al poderoso círculo de grandes amigos por brindarme su apoyo incondicional.

Y por supuesto, a mis padres y hermanos, quienes siempre han creído en mí.

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List of Figures

1.1 Development of gas sensing devices [1]. . . 2

1.2 Elements of a gas sensing system based on MOX gas sensors. . . . 3

1.3 Logarithmic compression circuit [2]. . . 4

1.4 Transresistance amplifier with reconfigurable gain [3]. . . 4

1.5 Oscillator-based Front-end circuits. . . 5

1.6 Interface architecture with the sensor isolated from the oscillator circuit [4]. . . 6

1.7 Resistance to frequency converter with multi-range scheme [5]. . . 6

1.8 On/off temperature control circuit [6]. . . 10

1.9 Heater resistance controller using clasic control aproach [7]. . . 10

2.1 Typical micromachined MOX gas sensor elements. . . 14

2.2 Sensing principle. (a) without CO and (b) with CO [8]. . . 16

2.3 Process of a sensor array with pattern recognition system [9]. . . . 20

2.4 Effect of operating temperature on the sensitivity to CO, H2, CH4 and C3H8 of SnO2 and SnO2 with additives [10]. . . 20

2.5 Resistance of a gas sensor as function of time under temperature modulation by a periodical signal [11]. . . 22

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xiv LIST OF FIGURES

2.6 Operation with a pulse drive in the heater: (a) heater voltage; (b) sensor temperature; (c) sensor output voltage. Ethanol and CO

concentrations are both 1000 ppm [12]. . . 22

2.7 hourly CO concentrations from Atmospheric Monitoring System (SIMAT) of Mexico city. . . 23

2.8 Typical response to CO of the AS-MLC gas sensor (from the datasheet). 25 2.9 Static characterization of the heater resistance. . . 26

2.10 Schematic of the experimental setup for heater characterization. . 26

2.11 Approximate relationship: TH vs RH. . . 27

2.12 Effect of the environment temperature variations. . . 28

2.13 Dynamic response of the heater to voltage step. . . 28

2.14 Schematic of gas sensor test bench. . . 30

2.15 Implementation of gas sensor test bench. . . 30

2.16 Response of the MOX gas sensor to CO exposure. . . 31

3.1 Resistance-to-Period/Frequency converters. . . 35

3.2 Biasing the sensor. . . 35

3.3 Noise in voltage biasing. . . 36

3.4 Noise in current biasing. . . 37

3.5 Generation of a time dependent signal. . . 39

3.6 Timing reference and the pole patterns involved. . . 39

3.7 Generation of a periodic signal. . . 40

3.8 Possible waveform produced by a first order oscillator. . . 40

3.9 A standard CMOS first-order oscillator. . . 41

3.10 General Resistance-to-Period converter. . . 43

3.11 Errors in a Resistance-to-Period converter. . . 44

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LIST OF FIGURES xv

3.13 Effect of the comparators offset. . . 47

3.14 Offset cancellation with matched comparators. . . 48

3.15 Output signal with offset cancellation. . . 48

3.16 Capacitor connected to the positive terminal of both comparators [13]. . . 49

3.17 Delay considerations on the standard CMOS RTC. . . 50

3.18 Proposed RTC. . . 53

3.19 Effect of the offset in the waveform of the output signal. . . 53

3.20 Transitions of both feedback loop. . . 55

3.21 Simple Resistance-to-Current Converter using two FVFs. . . 57

3.22 R-I converters reducingIerror at the output: a) RCC with different FVFs and b) RCC with a class-AB FVF. . . 58

3.23 Inverse of the output current vs sensor resistance for the simple and asymetrical RCCs. . . 59

3.24 R-I converters reducing reference voltages: a) RCC with only one reference voltage and b) RCC without reference voltages. . . 60

3.25 R-I converter with wide dynamic range FVF and improved current mirrors. . . 61

3.26 Deviations in the voltage across the resistor: a) due to resistance variations and b) due temperature variations. . . 61

3.27 Relative error between Iout and ix. . . 62

3.28 Resistance-to-Current Converters without current subtraction. . . 62

3.29 Current-to-Period Converter. . . 63

3.30 Generalized proposed topology. . . 65

3.31 Experimental set-up. . . 66

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xvi LIST OF FIGURES

3.33 Physical implementation of the R-T converters with RS biased by

two buffers with β= 100. . . 67

3.34 Comparison between simulations and measurements. . . 68

3.35 Fit to ideal equation. . . 69

3.36 Power consumption. . . 69

3.37 Performance of the R-T converter with RS biased with two buffer for β = 100. . . 71

3.38 Phisycal implementation of the R-T converter with RS biased by two buffers for β = 1. . . 72

3.39 Fit to ideal equation. . . 73

3.40 Performance of the R-T converter with RS biased with two buffers for β = 1. . . 74

3.41 Eschematic of the R-T converter with RS grounded. . . 76

3.42 Phisycal implementation of the R-T converter with RS grounded. 76 3.43 Fit to ideal equation: R-T converter with RS grounded. . . 77

3.44 Performance of the R-T converter with RS grounded. . . 78

3.45 R-T converter with two different FVFs. . . 80

3.46 Phisycal implementation: R-T converter with two different FVFs. 81 3.47 Fit to the ideal equation. . . 81

3.48 Performance of the R-T converter with R₋I converter using two different FVF. . . 82

3.49 R-T converter with two Flipped-Voltage-Follower type P. . . 84

3.50 Phisycal implementation: R-T converter with two FVF type P. . . 84

3.51 Performance of the R-T converter with R₋I converter using two FVF type P. . . 85

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LIST OF FIGURES xvii

4.1 On/off temperature control circuit. . . 93

4.2 Proposed control circuit. . . 93

4.3 Equivalent circuit when CLK=0. . . 94

4.4 Equivalent circuit when CLK = 1. . . 96

4.5 Electrical model of the heater. . . 97

4.6 Implementation of the heater model. . . 98

4.7 Approximate relationship: TH vs RH. . . 99

4.8 Operation with a pulse input. . . 100

4.9 Operating temperature at different levels. . . 101

4.10 Temperature modulation following a periodic waveform. . . 101

4.11 Layout and photograph of the temperature control system. . . 102

4.12 Temperature heater relationships. . . 103

4.13 Experimental response to input pulse. . . 104

4.14 Experimental measurement showing the operation of the control system. . . 105

A.1 Implementation of the biasing circuit. . . 114

A.2 OTA with NMOS input. . . 114

A.3 OTA with PMOS input. . . 115

A.4 Comparator. . . 117

A.5 Layout of the comparator. . . 118

A.6 Logic gates schematic at transistor-level. . . 118

A.7 The D-type flip flop. . . 119

A.8 Instrumentation amplifier. . . 119

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List of Tables

1.1 Comparison of the readout circuits . . . 7

1.2 Comparison of the temperature control circuits . . . 11

2.1 Base-line resistance of commercial CO gas sensors. . . 17

2.2 Comparison of commercial CO gas sensors. . . 24

2.3 Characteristics of the sensor (from the datasheet). . . 24

2.4 Characteristics of the MFCs (FATHOM GR series). . . 30

3.1 Deviations of the period: R-T converter with RS biased with two

buffers for β = 100 . . . 70

3.2 Parameters of R-T converter with RS biased with two buffers. . . 72

3.3 Deviations of the period: R-T converter with RS biased with two

buffers for β = 1 . . . 75

3.4 Parameters of R-T converter with RS biased with two buffers for β = 1. . . 75 3.5 Deviations of the period: R-T converter with RS grounded. . . 79

3.6 Parameters of R-T converter with RS grounded. . . 79

3.7 Deviations of the period: R-T converter with R₋I converter using two different FVF. . . 83

3.8 Parameters of R-T converter withRI converter using two different

FVF.. . . 83

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xx LIST OF TABLES

3.9 Deviations of the period: R-T converter withR₋I converter using two FVF type P. . . 86

3.10 Parameters of R-T converter with R-I converter using two FVF type P. . . 87

3.11 Comparison of the measured converters . . . 89

4.1 Parameters of the heater and electrical model of the AS-MLC com-mercial gas sensor. . . 98

4.2 Measured parameters of the control system. . . 103

4.3 Comparison of the temperature control circuits . . . 106

A.1 Parameters of the OTA with NMOS input. . . 115

A.2 Parameters of the OTA with PMOS input. . . 116

A.3 Parameters of the comparator. . . 117

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List of Acronyms

ABRCC Resistance-to-Current Converter with class-AB FVF CCO Current Controlled Oscillator

CMOS Complementary Metal-Oxide-Semiconductor DSP Digital Signal Processing

FVF Flipped Voltage Follower I-T Current-to-Period Converter MEMS Microelectromechanical System MFC Mass Flow Controller

MIM Metal Insulator Metal MOX Metal-Oxide

NAAQS National Ambient Air Quality Standards NMOS Negative-Channel Metal-Oxide-Semiconductor

NPRCC Resistance to Current Converter that uses NMOS and PMOS NPRCC Resistance-to-Current Converter with NMOS and PMOS transistors PID Proportional-Integral-Derivative

PMOS Positive-Chanel Metal-Oxide-Semiconductor PVT Process-Voltage-Temperature

R-I Resistance-to-Current Converter RTC Resistance-to-Period Converter

SRCC Simple Resistance-to-Current Converter SoC System on Chip

VCO Voltage Controlled Oscillator VLSI Very Large Scale Integration

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Chapter 1 Introduction

The concentration of certain gases in the air is a key parameter to be measured in a wide range of applications and different areas: safety, process control, au-tomotive, medicine, indoor air quality and environmental control. In particular, the increasing interest in the environmental protection was the main factor that motivated the development of low-cost and portable gas sensing systems in the last decade. Figure 1.1 shows the development of commercial gas sensing devices through the years up to the current small size devices. Several types of gas sen-sors have been developed so far: calorimetric, optical, electrochemical, piezoelec-tric and semiconductor metal oxide (MOX) gas sensors (also called chemiresistive sensors). Although MOX gas sensors were introduced to the market in 1968 by Figaro company, they have not been used in commercial gas sensing at industrial level due to their poor selectivity and stability. However, there is great interest from the industrial and scientific world in these sensors because of their numer-ous advantages. MOX gas sensors have small size, high sensitivity to detect low gas concentrations (in the order of ppb), short response and recovery time, and, due to the compatibility with CMOS technology, low cost. In contrast, tradi-tional analytical instruments such as mass spectrometers and chromatographs are expensive, complex and big in size.

A portable gas sensing system based on MOX gas sensors is generally composed by three elements (Figure 1.2): MOX gas sensor or array of sensors, electronic

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2 1.1. Previous work on interface circuits

Figure 1.1: Development of gas sensing devices [1].

interface circuit and data processor. The interface circuit, in turn, is composed by a read-out circuit and a temperature control system. Advances in microelec-tromechanical systems (MEMs) have led to the development of MOX gas sensors capable of reaching operating temperatures around 300-400◦

C in a few millisec-onds, with power consumption in the order of tens of milliwatts. As for the data analysis, several algorithms for pattern recognition and the use of different oper-ation modes of the sensors have been proposed in the literature, thus alleviating the shortcomings of MOX gas sensors. However, electronic interface circuits have so far been given less emphasis, and have been implemented with discrete com-ponents or integrated circuits that require complex calibration procedures, which increases the cost of the gas sensing system. The development of simple, low cost and low power electronic interface circuits is required to implement portable and low cost gas sensing systems. It is also just as important that the electronic inter-face circuits be robust to temperature and supply voltage variations in practical applications.

1.1 Previous work on interface circuits

As was shown in Figure 1.2, the interface circuit consists of two blocks: a read-out circuit and a temperature control circuit. The sensing layer resistance variations due to the gas or gases at which the sensor is exposed are conditioned by the read-out circuit, so that they can be handled by the data processor. The temperature control system, in turn, sets the working temperature of the sensor at the optimum level.

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Chapter 1. Introduction 3

Data processing

Gas or gases _{concentrations}

Sensor

or

array sensors

Read-out circuit

Temperature control circuit

+

Electronic Interface circuit

..

.

Figure 1.2: Elements of a gas sensing system based on MOX gas sensors.

1.1.1 Read-out circuits

Signal conditioning circuits based on voltage dividers and Wheatstone bridges followed by differential or instrumentation amplifiers have been widely used in the literature. Nevertheless, this approach is only suitable for sensors whose relative variation in resistance is much lower than unity. This is true even if lineariza-tion techniques are applied, due to the intrinsic limitalineariza-tion in the dynamic range [14]. Different solutions have been proposed to handle the wide range resistance variations of MOX gas sensors, which can be classified into the following groups:

Logarithmic converter This method is based on establishing a logarithmic re-lationship between the sensor resistance and the output voltage, in order to handle a wide range resistance variation while preventing the saturation imposed by the available supply voltage in sub-micron CMOS technologies. In [2] (Figure 1.3), after a calibration process the achieved accuracy is 2% in 4 decades. The main drawback of using this method is the mismatch between diodes. Also, the relationship is nonlinear due to compression with a logarithmic function.

Multi-scales: It consists in dividing the whole range into several sub-ranges. Thus, the limitation given by the available supply voltage is avoided, and accuracy is maintained in each sub-range. The work in [3], shown in Figure 1.4, achieves an accuracy of 0.1% in 5 decades by controlling the gain in

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Figure 1.3: Logarithmic compression circuit [2].

the transresistance amplifier according to the sensor resistance. However, it requires a complex calibration process that raises the system cost and power consumption up to about 5 mW.

Figure 1.4: Transresistance amplifier with reconfigurable gain [3].

Oscillator-based: This technique can cover a wide resistance range without sub-ranges because it is not limited by the available supply voltage. In the literature, two approaches can be found: the first one places the sensor into an oscillator circuit, as shown in Figure 1.5, where the time constant directly depends on the sensor resistance. The circuits in [15, 16] report accuracies

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around 1%. The main disadvantage is that the accuracy can be affected by the sensor parasitic capacitance, and the necessity to add high-performance amplifiers and comparators.

(a) Ferri et. al.[15]. (b) Merino et. al.[16].

Figure 1.5: Oscillator-based Front-end circuits.

The second approach isolates the sensor from the oscillator circuit to avoid the effect of the sensor capacitance [17, 18, 19, 20], as shown in Figure 1.6. The circuit places a constant voltage across the sensor, which generates a current which depends on the value of the sensor resistance. The generated current is driven to the charge and discharge circuit which, in turn, generates a periodic signal whose period depends on the resistance value. The work by Conso [4] achieves an accuracy better than 2% without complex calibration process over 6 decades of resistance variation and better than 0.6% over 3 decades. It is important to note that this method can also be combined with the multi-range technique, as shown in Figure 1.7, in order to reduce the signal acquisition time for resistance values in the order of hundred of Mega-Ohms, as well as to reduce power consumption for resistance values in the order of hundred Ohms [5].

In Table 1.1 the performance of readout circuits with different techniques are compared. As seen, several read-out circuits have been proposed so far to handle the wide range resistance variation of MOX gas sensors. Some of them get accu-racy better than 1% after a calibration process. However, there are still two issues

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Figure 1.6: Interface architecture with the sensor isolated from the oscillator circuit [4].

Figure 1.7: Resistance to frequency converter with multi-range scheme [5].

that have not been fully addressed in the design of interface circuits for portable low cost gas sensor microsystems:

• Robustness: For practical implementations, it is necessary that circuits be

able to keep the same response under changes in power supply and temper-ature.

• Reducing complexity: The front-end readout circuit can be accurate and

ro-bust, but this strongly depends on the accuracy and precision of the reference signal and on the robustness of the building blocks (amplifiers, comparators, resistors, capacitors, etc.). Therefore, when aiming for accuracy and robust-ness the whole system can become complex, increasing its cost and power consumption.

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Table 1.1: Comparison of the readout circuits

Parameter Readout circuits

[2] [3] [15] [4] [5] CMOS Technology 0.8µm 0.35µm 0.35µm 0.35µm 0.13µm

Supply voltage 5V 3.3V 3.3V 3.3V 1.2V Input resistance range 1kΩ−10MΩ 100Ω−20MΩ 1MΩ−10GΩ 1kΩ−1GΩ 800Ω−100MΩ

Linearity error – – 1% 1.8%* 5%**

Accuracy 2% 0.13% – – –

Deviation due to∆T emp – – ±5% (0-80◦_C) _– _–

Power Consumption – 6mW 4mW ∼3mW 0.33mW Area – 6.5mm2 _0.84mm2 _– _0.012mm2 *0.6% over 3 decades, **Linearity error on each sub-range

All previously published work focuses on achieving a large dynamic range with little emphasis on robustness and with the need for auxiliary high-performance circuits or elements.

1.1.2 Temperature control circuits

Modulation of the working temperature is one of the most attractive methods to enhance the performance of MOX gas sensors [21]. Different modes of tempera-ture operation have been proposed in the literatempera-ture [10]. The most common are the following: operation at different temperature levels, operation with tempera-ture pulses and temperatempera-ture modulation following periodic waveforms (sine-wave, triangular, square, ramp and others). The operating temperature plays an impor-tant role in the sensor response, because sensor sensitivity to a particular gas or gases depends on the sensing layer temperature. Consequently, a good tempera-ture control is needed to improve the performance of gas sensing systems based on MOX gas sensors.

In the literature different methods for controlling the working temperature have been proposed [22]; however, most of them make use of an additional temperature sensor embedded near the heater [2, 17, 23, 24]. A temperature control system that uses an additional temperature sensor is not directly applicable to commercial MOX gas sensors, which do not have an embedded temperature sensor and only

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have four available terminals: two terminals for the heater and two others for the sensing layer.

The temperature of a commercial MOX sensor is established in most cases by a specific voltage applied to the heater [25, 26], rather than by controlling the oper-ating temperature. Consequently, the environment temperature variations modify the sensor response, as the sensor temperature changes even if the heater voltage remains constant [27, 28]. In [29] it was shown that the sensor operating temper-ature and, therefore, its response remain almost independent of the environment temperature variations if the heater resistance is kept constant.

In the literature, solutions have been proposed where the operating temperature of the sensor is controlled by directly measuring the heater resistance, that is, by using the heater resistance itself as a temperature sensor [30, 31, 6, 7, 32]. In this sense, two control schemes have been applied:

On/off control: The implementation is simple and low cost because it does not require a detailed model of the heater and is always stable. Furthermore, the minimum supply voltage required to achieve the desired temperature only depends on the heater power consumption, because the transistor that drives the heater does not need to be in saturation, as it only acts as a switch. Figure 1.8 shows the control circuit proposed in [6]. The system operation is divided in two phases controlled by a clock: measuring phase and heating phase. When the clock is high, the measuring stage is activated, and the heater is biased with a constant known current. The voltage across the heater is measured and compared with an external reference value (which represents the desired heater resistance) and the result of the comparison is used in the second stage. In the second stage (when the clock is low), the biasing current is disconnected. Depending on the result of the comparison, the heater is supplied with a maximum supply voltage or is disconnected from it, in order to be heated, in the first case, or cooled, in the second case. Simulation results of the circuit shown in Figure 1.8 have shown an error in the order of _±1◦

C. In [33] experimental results using the on/off control technique can be found, showing a ripple of heater resistance of

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about _±2.5Ω, that corresponds to a temperature ripple of about _±7.5◦

C in the best case.

Classic control: In general this approach requires a larger number of blocks or operations than the on/off control. Furthermore, the transistor that drives the heater needs to be in saturation, so a larger device is necessary to avoid electromigration problems due to the high power consumption. A control circuit using the classic control, proposed in [7], is shown in Figure 1.9. The control circuit uses a Proportional-Integral-Derivative (PID) block, whose parameters are adjusted empirically. The reference voltagesV RN andV RP

set the maximum and minimum temperature of the heater. The system op-eration consists in measuring both the current and the voltage across the heater; the measured values are converted to the digital domain and the heater resistance is calculated by the microcontroller. Subsequently, the cal-culated heater resistance is compared to a reference value; according to the result, the voltage across the heater is increased, decreased or maintained, in order to achieve the desired temperature level. The system was simulated with two gas sensors, obtaining an error lower than 1◦

C. Moreover, the capacity of the control circuit to vary the heater resistance following a sinu-soidal waveform was simulated. The circuit can change the heater resistance periodically with frequencies up to 25Hz. This value is much higher than the mili-Hertz frequency used in the temperature modulation techniques.

In Table 1.2 the characteristics of both on/off and classic temperature control techniques are compared. As seen, the more simple and efficient solution to con-trol the working temperature of MOX gas sensors is the on/off concon-trol technique. In contrast, the solutions using the classic control technique need higher supply voltage, digital data processing and therefore higher power consumption. How-ever, classic control (Figure 1.9) has an useful advantage in practical applications, which is the flexibility to control different MOX gas sensors.

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Figure 1.8: On/off temperature control circuit [6].

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Table 1.2: Comparison of the temperature control circuits

Parameter Temperature control circuits [6] [7] CMOS Technology – 0.8µmHigh-Voltage Supply voltage 5V 5V and 15V Resolution 2◦_C ₁◦_C

Heater resistance range 50-100Ω 50-100Ω

310-800Ω

Power Consumption ∼2.18mW – Complexity Low High

1.2 Objectives

The main objective of this thesis is to develop practical architectures for signal conditioning and temperature control of MOX gas sensors to interface the most up-to-date sensors with the new techniques used to improve the sensor’s performance. Thus, the interface circuits can be used for developing portable, reliable, low-cost gas-sensing systems.

On the one hand, the readout topology must meet the following aspects:

• be able to handle a range of sensor resistance variation in the order of at least three decades.

• keep power consumption lower than 1mW.

• be robust to _±10% of supply voltage variations and environment tempera-ture variations from 0 to 85◦

C (commercial range).

• have the capacity to handle MOX gas sensors with different baseline resis-tances.

On the other hand, the temperature control circuit must meet the following spec-ifications:

• keep power consumption lower than 3.5mW, which is at least ten times lower than the power consumption of heaters in current MOX gas sensors.

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12 1.3. Thesis Organization

• establish the operating temperature of MOX gas sensors with an accuracy of at least _±5◦

C.

• have the capacity to apply temperature modulation techniques.

• provide flexibility to handle MOX gas sensors with different requirements in power consumption to achieve the desired operating temperature.

All circuits will be designed and integrated in a 1.8V 0.18µm CMOS technology process.

1.3 Thesis Organization

In this chapter a review of the state of the art of the conditioning circuits for MOX gas sensors namely, readout and temperature control circuits has been carried out, and, the objectives of the thesis have been established. In Chapter 2, the gas sens-ing principle and the main characteristics of MOX gas sensors are presented. In addition, the characterization of a commercial MOX gas sensor (AS-MLC from AppliedSensor) for the detection of carbon monoxide (CO) is described, so as to extract the parameters required for the design of the circuits. In Chapter 3, the architecture of the proposed read-out circuit is discussed. Four implementations based on the presented general Resistance-to-Period Converter are proposed and, finally, experimental results are shown. In Chapter 4, the proposed tempera-ture control circuit is described. It consist of an on/off temperatempera-ture control that handles the operating temperature of the sensor using the heater resistance itself as temperature sensor, which have the capacity to handle heaters with different requirements on power consumption. Both simulation and experimental results with the commercial gas sensor AS-MLC are shown. Finally, in Chapter 5, the conclusions of the thesis are drawn, and possible future research directions are discussed.

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Chapter 2 MOX gas sensors

In 1962, Seiyama et al. demonstrated that adsorption/desorption processes on a semiconducting metal oxide heated to _∼ 300◦

C produce a strong change in the film conductivity [34]. Currently, the semiconducting gas sensors based on metal oxides (MOX gas sensors), also sometimes called chemiresistive gas sensors, constitute one of the most interesting and attractive solutions sensor for portable applications. Compared to other type of gas sensors, MOX devices show higher sensitivity (in the order of parts per billion (ppb)), low cost, small size, and the facility of co-integration with electronics circuits on the same chip [35].

Several metal oxides like SnO2, TiO2, In2O3, ZnO or WO3, among others, have

been investigated to build MOX gas sensors [36]. From those, tin dioxide (SnO2)

is one of the best-investigated and the most common choice as material for gas sensors [8, 37]. The electrical and optical properties have been extensively investi-gated because Tin dioxide has been used in many applications such as transparent electrodes, resistors, transparent heating elements and other part in electric equip-ment where transparency is required [38].

The development of MOX gas sensors has been possible thanks to the advances of two technologies: new nanostructured materials and the development of micro-electro-mechanical system (MEMS). The first has provided sensors with higher sensitivity, selectivity and stability, and smaller size. The second allows to achieve the typical operation temperature in the order of 100 to 500◦

C, with relatively

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14 2.1. Gas sensing principle

a low power consumption and low thermal time constant. Currently, MOX gas sensors can be heated to 400◦

C in a time of milliseconds with power consumption in the order of milliwatts [39].

2.1 Gas sensing principle

The operation principle of gas sensors is based on the chemical interaction of the species of interest with the active material, which result in a change of a physical parameter, such as electrical current, voltage, resistance, light intensity, mass, temperature or pressure. Measuring some of these physical parameter variations make it possible to determine the concentration of the chemical species in the environment. In the case of MOX gas sensors, the detection principle is based on changes in the sensing layer resistance.

The structure of a micromachined MOX gas sensor consists of a sensing layer based on a metal oxide semiconductor deposited on a substrate, and an integrated heater (Figure 2.1). The heater allows to achieve the desired operation temperature of the sensing layer, which is in the order of 100 to 400◦

C to get the best sensitivity [10]. Besides the cross section and top view of the sensor, in Figure 2.1, the most simple electrical representation of the MOX gas sensor is also shown. The heater is represented by a resistor RH and the sensing layer by a variable resistor RS.

Sensing layer

Heater

Electrodes

Sensing layer terminals

Heater terminals

Si Si

Rs

R_H

Cross section:

Top view:

Simple electrical model:

Figure 2.1: Typical micromachined MOX gas sensor elements.

The exact mechanism involved in the sensing process of MOX gas sensors is not yet fully understood. However, a simple and widely used model to represent the response of an n-type sensor (e.g. SnO2) is expressed by:

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Chapter 2. MOX gas sensors 15

e−+ 1

2O2 ⇋O

−

(2.1)

This equation express the adsorption of atmospheric oxygen, which traps electrons from the conduction band, causing ionization of the atom. The ionized atom can then react with a reducing gas such as CO, forming CO2 and releasing electrons,

which return to the conduction band, as represented by:

CO+O−

→CO2+e

−

(2.2)

The changes in the charge carrier concentration produce a resistance change be-tween the electrodes of the sensing layer. This sensing process is depicted in Figure 2.2: the figure shows the behaviour in the structural and band model. The oxygen charged due to the adsorption process on the surface repels other electrons, creating a depletion region, which increases the potential barrier at the grain boundaries (Figure 2.2a) and thus increases the resistance. When the sensor is exposed to CO, CO is oxidized by CO−

and lowers the potential barrier and the depletion region because the electrons are released, as shown in Figure 2.2b. In this way, the electrons can flow more easily through different grains and thus the resistance is decreased. The sensor resistance is, therefore, a function of the gas concentration. In practice, the relationship between sensor resistance and gas concentration can be described by:

RS ∼=K·C

±_n

(2.3)

whereCis the target gas concentration, andK andn are measurement constants. The constant n is positive for oxidizing gases and negative for reducing gases.

In an oxygen-free atmosphere, CO acts as electrons donor: it is adsorbed as a CO+ _{ion, thus inserting an electron into the conduction band [40].}

O2 only moves

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16 2.1. Gas sensing principle

Figure 2.2: Sensing principle. (a) without CO and (b) with CO [8].

2.1.1 Sensing layer resistance

The sensing layer resistance of MOX gas sensors may vary across several decades, due to the effect of three variable components:

• The baseline resistance Rbaseline, or resistance in clean air, which typically

ranges from kilo-Ohms to Mega-Ohms, depending on the fabrication process and the gas to detect. Table 2.1 shows the base-line resistance of some commercial MOX gas sensors from different companies. Notice that some of them have Rbaseline in the order of tens of kilo-Ohms, while others reach

tens of Mega-Ohms;

• The deviation from the baseline resistance∆Rbaseline, which can be as large

as one decade, due to aging, operation temperature and humidity.

• The resistance variation due to gas concentration ∆Rgas, which can be as

large as a couple of decades with respect to the baseline resistance.

Thus, the resistance value can be expressed as:

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Table 2.1: Base-line resistance of commercial CO gas sensors.

Commercial sensor* Base-lineresistance (kΩ) Detection range (ppm) Typical Minimum Maximum

TGS-2201 - 10 80 10∼1000 (CO,H2,HC) TGS-2201 - 100 2000 0.1∼10 (NO,NO2) MSGS-3002 1000 - - 100∼10000 (CH4) MSGS-3001 36000 - - 5∼1000 (CO) AS-MLC-2600 100 - - 0.5∼500 (CO) *MiCS(e2v); TGS(Figaro); MSGS(Microsens SA); AS-MLC(Appliedsensor)

The large variation in the MOX gas sensor resistance and the requirement im-posed on the minimum resolvable resistance change (minimum measurable gas concentration change) translate into a wide dynamic range specification for the read-out circuits.

Although at first, due to poor control in fabrication steps of the sensitive layer, the base-line resistance had high variability, it is currently possible to manufacture MOX gas sensors with a more precise Rbaseline value. Thus, in the development of

gas microsystems with a specific sensor, the dynamic range requirement is mainly reduced to ∆Rbaseline y ∆Rgas.

2.2 Gas sensing parameters

In order to describe the performance of a gas sensor, a set of parameters are used [41]. The main parameters which determine the reliability of MOX gas sensors are:

Sensitivity Response capacity of the sensor to an amount from gas concentra-tion.

Selectivity Capacity to discern the gas or gases of interest of others.

Stability Characteristic of the sensor to give reproducible measurements for a certain period of time: this means that the sensing layer retain its characte-ristics after adsorption and desorption of gases in the environment (regen-eration).

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18 2.2. Gas sensing parameters

Operating temperature The characteristics of MOX gas sensor are highly tem-perature dependent. So the operating temtem-perature is, usually, the temper-ature at which sensitivity is maximum.

Robustness Is the ability of the sensor to maintain its response to changes in humidity and environment temperature, as well as regeneration after pollu-tion. Unfortunately, the adjustment of parameters to improve robustness produces a decrease in sensitivity and selectivity [42].

Response and recovery time The response time is the time interval required for the sensor resistance to reach 90% of the final resistance value in response to a step concentration change from zero to a certain concentration value. Recovery time refers to the time it takes the sensor resistance value to reach 90% of its initial value (in clean air) after a certain concentration gas is retired.

Dynamic range Is the analyte concentration range between the lowest concen-tration that can be detected by the sensor and the highest concenconcen-tration before saturation.

An ideal MOX gas sensor would have high sensitivity, selectivity, stability and dynamic range; short response and recovery time; long life cycle and robustness. Optimizing the fabrication of a gas sensor is a difficult task because parameters are is not independent from each other. However, real applications usually do not require all of these ideal characteristics at once. For example, a gas sensor used to control a car combustion system needs a short response and recovery time. In contrast, for a sensor used in environmental monitoring, a response/recovery time of a few minutes can be acceptable. So, for specific applications scientists often make efforts to approach only some of the ideal characteristics or, in general, achieve the best compromise during the optimization process.

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2.3 Ways to improve performance

Despite the high sensitivity achieved by MEMS technology with the synthesis of new sensing layers while keeping moderately low power consumption, it still has some drawbacks with respect to selectivity and stability that complicate its practical application [43]. Currently, there are in general two focuses to overcome these drawbacks. The first one is to develop new material and technologies [44, 45]. The second one, consist in using the current development of MOX gas sensors with special operation modes. For example, an array of non-selective sensors can be used to generate a unique signature for each mixture of gases and with a pattern recognition algorithm the gases can be identified [46, 47]. Also, temperature modulation provides additional information because alters the kinetics of the gas-sensor interaction producing a characteristic response pattern for each gas or mixture of gases [10, 48].

Figure 2.3 shows the blocks and the process of a gas detection system using an array of gas sensors with a pattern recognition algorithm. The process consists of capturing a matrix data from the sensors array and, by means of a pattern recognition algorithm, establishing a relation between the output signal and the gas or gases present [47, 9, 46]. The main issue of this method is that a large collection of data is necessary during calibration i.e., exposing the sensor array to different known concentrations of gases and gas mixtures, thus increasing the system cost.

Figure 2.4 shows the variation with temperature in the sensitivity to carbon monoxide (CO), hydrogen (H2), methane (CH4) and propane (C3H8) with

tem-perature of three sensing layers: SnO2, Pt-SnO2, Pd-SnO2 and Ag-SnO2. It can

be seen in Figure 2.4 that maximum sensitivity occurs at different temperatures for different gases and different oxides. This behaviour has been well established for metal oxide gas sensors. This form of response is exploited to improve the sensor performance by temperature modulation. In the last years, modulation of the sensor working temperature has been the most common solution to overcome the poor selectivity of MOX gas sensors. Temperature modulation produces a response pattern dependent on the gas or gases mixture in the environment and

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20 2.3. Ways to improve performance

Figure 2.3: Process of a sensor array with pattern recognition system [9].

the way in which the operating temperature is varied [10]. Taking advantage of this property, several methods of temperature variation have been proposed in the literature. The most representative methods are:

Figure 2.4: Effect of operating temperature on the sensitivity to CO, H2, CH4

and C3H8 of SnO2 and SnO2 with additives [10].

• Driving the sensor at different operating temperature levels. Considering that the sensor has maximum sensitivity to a particular gas at different

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temperatures (this is equivalent to having a sensor array where each sensor is more sensitive than the others to a specific gas) [49, 50, 51, 52].

• Driving the working temperature of the sensor with a periodical waveform, such as a sine, triangular or square wave. In the literature analysis using different waveforms and frequencies are reported, obtaining the dynamic response characteristics to the gas or gases present in the test environment [11, 53, 54]. Best results are obtained when the temperature is modulated with a frequency in the order of mHz. Fig. 2.5 shows the response of an MOX gas sensor under temperature modulation with a periodical signal of 50mHz. It can be seen that the way the resistance varies depends on the gas or gases present.

• Producing a temperature pulse and sampling the transient response of the sensor. In [55, 56, 12] it was shown that the shape of the transient response during heating and cooling is characteristic of the gas to which the sensor is exposed (Figure 2.6). This technique was applied in [25] by relating the shape of the sensor response to the CO concentration, and the robustness of the method to environment temperature variations and to drift of the baseline resistance was shown. However, the tests were performed without the presence of any other gases.

2.4 Carbon monoxide MOX sensor

The demand for portable, low cost gas-sensing systems is growing every year, due to the increasing interest in monitoring the ambient pollution to limit the human exposure to dangerous gases. In this sense, the sensor used in this thesis was chosen to detect carbon monoxide (CO), as it is one of the most dangerous gases to human health. It is colorless and odorless, requiring devices for detection. CO combines preferentially with hemoglobin to produce carboxyhemoglobin (COHb), displacing oxygen and reducing systemic arterial oxygen content, so reducing oxy-gen delivery to the body’s organs. Consequently, relative minute concentration

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22 2.4. Carbon monoxide MOX sensor

Figure 2.5: Resistance of a gas sensor as function of time under temperature modulation by a periodical signal [11].

Figure 2.6: Operation with a pulse drive in the heater: (a) heater voltage; (b) sensor temperature; (c) sensor output voltage. Ethanol and CO concentrations are both 1000 ppm [12].

of the gas in the environment can result in toxic concentrations in human blood [57, 58, 59]. The maximum time weighted average exposure value sets by the

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Mexican Official Norm NOM-021-SSA1-1993 is 11 ppm over an 8 hours period while for the National Ambient Air Quality Standards (NAAQS) from United States is 9 ppm over an 8 hours period and 35 ppm for 1 hour. Figure 2.7 shows the hourly average of CO concentration in Mexico city in the last four years.

Figure 2.7: hourly CO concentrations from Atmospheric Monitoring System (SIMAT) of Mexico city.

According to CO concentration in the ambient air and the limit value ascribed by the standards, the selected gas sensor must be able to detect concentrations in the order of 1 ppm for environmental monitoring application.

In Table 2.2 three commercial MOX gas sensors sensitive to CO are compared. The AS-MLC from AppliedSensor presents the best characteristics with regards to sensitivity and power consumption, so it was the sensor used in this work.

The AS-MLC sensor uses a porous thick film of polycrystallineSnO2deposited on

a Silice-micro-machined substrate. In the structure, there is a heater that can heat the sensing layer from ambient temperature to_∼300◦

C. The main characteristics of this sensor are given in Table 2.3 and, Figure 2.8 shows its typical response to CO (from 10ppm to 50ppm).

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Table 2.2: Comparison of commercial CO gas sensors.

Parameter e2v AppliedSensor Figaro

MicCS-5525 AS-MLC TGS-2600

Dynamic range 1-1000ppm 0.5-500ppm 1-100ppm

Power consumption 76mW 35mW 210mW

Typical Operating 340◦_C ₂₇₀◦_C _— temperature

Response/ — seconds —

Recovery time.

Base-line resistance 100-1500kΩ ∼100kΩ 10∼90kΩ

Table 2.3: Characteristics of the sensor (from the datasheet).

Operational conditions

Typical operation temperature 270◦ C

Electrical characteristics

Power consumption 35mW at 270◦ C Typical sensor resistance during

operation in air (50% RH) 50 kΩrange Typical sensor resistance during

operation in 30 ppm CO (50% RH) 1 kΩrange

Heater

Typical heater voltage ∼2.3V for 270◦ C Temperature coefficient rel. to R(20◦_{C) TC}

≈1700 ppm/K Typical heater resistance 95Ω

Sensing properties

Sensitivity range 0.5-500ppm

Recovery time Seconds

Cross sensitivity to humidity, hydrogen and hydrocarbons

The sensor was tested in order to prove its functionality and to get some param-eters used in the design of the conditioning and temperature control circuits.

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Figure 2.8: Typical response to CO of the AS-MLC gas sensor (from the datasheet).

2.4.1 Experimental characterization of the heater

In the literature, MOX gas sensors with a temperature sensor embedded with the heater can be found. However, this not only increases the number of terminals, but also makes the calibration procedure could be more complex. Additionally, the implementation of accurate and temperature independent current sources would be necessary. For this reasons, MOX gas sensors available in the market mainly consist of two elements: the heater and the sensing layer. In this case, the heater is used as both the temperature sensor and the actuator.

The heater was characterized to complete the parameters given in the sensor datasheet (Table 2.3). Figure 2.9 shows the static characterization of the heater at 23◦

C (ambient temperature). The 2400 SourceMeter instrument from Keithley was used in the 4-wire mode to measure the resistance of the heater. The source was configured as voltage-source and the meter as current-meter, the schematic of experimental setup is shown in Figure 2.10. The heater voltage VH was varied

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a power consumption of _∼35mW when the temperature of the heater is about 270◦

C. The results show that the heater resistance varies from 102Ω at ambient temperature (VH ≃0V) to 151Ωat VH = 2.7V.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 100 110 120 130 140 150 TA=23ºC R e si st a n ce ( O h m s) Voltage (V)

(a) Heater resistance vs applied voltage.

0 10 20 30 40 50

100 110 120 130 140 150 R e si st a n ce ( O h m s) Power (mW) TA=23ºC

(b) Heater resistance vs applied power.

Figure 2.9: Static characterization of the heater resistance.

R_H _Rs

I-Meter

V-Meter _V

H

V-Source

Keithley 2400 SourceMeter

MOX gas sensor

Figure 2.10: Schematic of the experimental setup for heater characterization.

From the well-known temperature dependent expression for the resistance of met-als (RH(T) = RH0[1 +α(TH −TA)]), the relation between the heater resistance RH and its temperature TH is given by:

TH =

1

α

RH RH0 −

1

+TA (2.5)

whereαis the temperature coefficient of the heater,TAis the ambient temperature

and RH0 is the heater resistance at TA. Taking the temperature coefficient value

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we can get the approximate variation range of the heater temperature (see Figure 2.11). The relation shows a slope of 5.76◦

C/Ω or the inverse relation 0.17Ω/◦

C. Therefore, in order to control the operating temperature in the order of _±1◦

C, it is necessary to control the heater resistance in the order of _±0.2Ω.

100 110 120 130 140 150

0 50 100 150 200 250 300

T

H

(

º

C

)

R H

(Ohms)

slope = 5.76ºC/

Figure 2.11: Approximate relationship: TH vs RH.

Ideally, for a given supply voltage applied to heater the sensor reaches a certain temperature. However, external factors as environment temperature variations produce variations in the sensor temperature [60]. The effect of the environmental temperature variations on the heater resistance is shown in Figure 2.12. The heater was biased with a constant voltage of 2.5V and was exposed to a heat source generated by a soldering station with temperature controller. The heater resistance varied about 3.2Ωfor an environmental temperature variation of 37◦

C. Considering the ideal relation given by Equation 2.5, the variations in the heater resistance correspond to variations in the operation temperature of about 18◦

C. The results show, therefore, that a constant supply voltage may not be suitable to keep constant the working temperature of the sensor. Giberti et al. showed that the influence of environmental temperature variations can be reduced by maintaining the heater resistance at a constant nominal value [28].

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0 200 400 600 800

148.5 149.0 149.5 150.0 150.5 151.0 151.5 152.0 152.5 Heater resistance Environment temperature Time (s) R e si st a n ce ( O h m s) 25 30 35 40 45 50 55 60 65 70 T e m p e r a t u r e ( º C )

Figure 2.12: Effect of the environment temperature variations.

shows the dynamic response of the heater, showing a thermal time constant of 22ms, which is the time when the heater resistance reaches a 63% of the total variation.

0 10 20 30 40 50 60 70 80 90 110 120 130 140 150 Heater resistance Heater voltage Time (ms) R e si st a n ce ( O h m s) = 22ms 0.5 1.0 1.5 2.0 2.5 V o l t a g e ( V )

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2.4.2 Experimental characterization of the sensing

resistance

The main goal of this characterization is to know the change in the sensing layer resistance due to small changes in the CO concentration, so as to determine the minimum resolvable change required for the readout circuit to detect gas con-centrations lower than 1 ppm. In this sense, readout circuits based on lock-in amplifiers have shown the capability to detect gas concentrations with a resolu-tion lower than 1 ppm, being estimated that even, so as gas detecresolu-tion in the order of ppb is possible [61, 62, 63, 64].

The schematic and physical implementation of the experimental setup are shown in Figure 2.14 and Figure 2.15, respectively. The experimental setup comprises the gas delivery system, a test chamber and the source-meters for sensor response acquisition. The gas delivery system includes gas cylindres (carbon monoxide and nitrogen), pressure regulator, mass flow controllers (MFCs) and mixing containers. The measurements were carried out in a controlled environment of nitrogen to avoid cross-sensitivity to other gases. Table 2.4 provides the main characteristics of the MFCs from FATHOM GR series. The concentration of CO in the test chamber is given by the following relations:

Mixture1(%) =

sccm1

sccm2+sccm1 ·

100 (2.6)

Mixture2(%) =

sccm4

sccm2+sccm4 ·

Mixture1 (2.7)

CO(ppm) = sccm5 ·Mixture2

sccm3 +sccm5 ·

104

(2.8)

where sccm1 to sccm5 are the flow in standard cubic centimeters per minute of

each MFC.

The temperature in the test chamber was 22.7◦

C with a relative humidity of 12%, which were monitored with a DHT22 temperature-humidity module from AOSONG. Although the sensor is exposed to an atmosphere containing oxygen

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MFC₁

MFC2

MFC₃

MFC4 MFC5

CO

N₂

Mixture₁ CO+N₂

Mixture₂ CO+N₂

Mixer

Gas sensor test chamber

GPIB

Source-meters

Figure 2.14: Schematic of gas sensor test bench.

Figure 2.15: Implementation of gas sensor test bench.

Table 2.4: Characteristics of the MFCs (FATHOM GR series).

Parameter MFC1,4,5 MFC2,3

Range 250sccm 5000sccm

Accuracy ±1% FS ±1% FS Repeatability ±0.15% FS ±0.15% FS Max. Pressure 250psi 250psi

Response time 1s 1s

Temp. Coefficient 0.05% FS/◦_{C 0.05% FS/}◦_C Pressure coefficient 0.01% FS/psi 0.01% FS/psi

in most applications, experimental test using only nitrogen also provides infor-mation such as response time and the ability of the sensor to detect small gas concentrations.

The gas delivery system was configured to provide 1, 2 and 3ppm in the test chamber. The AS-MLC MOX gas sensor was exposed to these gas concentrations

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alternating with only nitrogen flux in order to see the recovery capacity of the sensor and to clean the test chamber. In Figure 2.16, the resistance variation of the sensing layer for the three concentrations of gas are shown. In Figure 2.16a the dynamic response shows that the sensor has a response and recovery time in the order a few minutes when the gas concentration of CO is introduced and removed from the test chamber respectively. It is worth mentioning that the time response includes the gas delivery system response time, which was not characterized. In the sensor’s datasheet, the recovery time is claimed to be in the order of seconds. So, the response time of the test system is much higher than that of the sensor. However, it was out of the scope of this work to characterize the time response of the gas mixing system. Figure 2.16b shows the sensing layer resistance when the sensor is exposed to 1, 2 and 3ppm of carbon monoxide. The variation in the sensing layer resistance due to CO was about 3kΩ per ppm, in other words, the sensor resistance present a relative variation about 8% when the CO concentration increases or decreases 1 ppm. As a result, the readout circuit handling the AS-MLC gas sensor must have an accuracy better than _±4% to detect gas concentration lower than 1 ppm.

0 50 100 150 200

2x10 4 3x10 4 4x10 4 5x10 4 6x10 4 7x10 4 8x10 4 9x10 4 3ppm 2ppm R e si st a n ce ( O h m s) Time (min) Sensing layer resistance

1ppm TA=22.7ºC RH=12%

(a) Dynamic response to CO exposure.

1.0 1.5 2.0 2.5 3.0

3.2x10 4 3.4x10 4 3.6x10 4 3.8x10 4 4.0x10 4

Sensing layer resistance

R e si st a n ce ( O h m s)

Carbon monoxide (ppm)

(b) Resistance of sensing layer vs CO.

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32 2.5. Conclusions

2.5 Conclusions

In this chapter, the experimental characterization of the commercial AS-MLC gas sensor was done. Such characterization is the starting point for the design of the interface circuit. On the one hand, the sensor was exposed at 1, 2 and 3 ppm of CO, the sensor response showed a relative variations about 8%/ppm on its resistance. Therefore, the readout circuit must determine the sensor resistance with a relative error below of 4% to detect variations in the order of 1 ppm of the gas concentration. Also, the readout circuit needs to handle a range of resistance variations from a few kΩ to hundreds of kΩ according to the typical response and the experimental measurements of the commercial AS-MLC gas sensor. On the other hand, the heater resistance was also characterized, in particular, the thermal time constant and the range of heater resistance variation versus the applied voltage to its terminals. The AS-MLC gas sensor showed a thermal time constant of 22ms and a range of heater resistance variation from 102 to 151 when the heater voltage was varied from 0 to 2.7V. From the characterization, it was determined that in order to control the operating temperature in the order of

±1◦

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Chapter 3 Read-out circuits

The advancements in digital signal processing (DSP) and very large scale inte-gration (VLSI) have favored the implementation of many analog functions in a digital way with lower cost and more robustness to variations in process, volta-ge and temperature (PVT). However, even if most Systems-on-Chip (SoCs) use digital circuits, they need to be interfaced with the external world by analog front-end circuits. Therefore, analog circuits for the implementation of interfaces will remain irreplaceable and, furthermore, their characteristics are often very critical for the overall performance of the System. In the case of gas sensing systems, a miniaturized MOX gas sensor combined with suitable low power, low cost in-tegrated interface circuit offers the possibility to make low power and low cost chemical detection microsystems.

As explained in Chapter 2, the MOX gas sensor resistance value may vary across several decades, combining the effect of three variable components and can be expressed as:

RS =Rbaseline+ ∆Rbaseline+ ∆Rgas (3.1)

whereRbaselineis the resistance in clean air, which typically ranges from kilo-Ohms

to Mega-Ohms, depending on the fabrication process. ∆Rbaseline is the deviation

from the Rbaseline, which can be as large as one decade, due to aging, operation

temperature and humidity. Finally, ∆Rgas is the resistance variation due to the

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34

3.1. Resistance to Period Converters: Building Blocks

gas concentration, which can be as large as a couple of decades with respect to

Rbaseline. The large variation in the MOX gas sensor resistance and the

require-ment imposed on the minimum resolvable resistance change (minimum measurable gas concentration change) translate into a wide dynamic range specification for the read-out circuits.

This chapter presents a new Resistance-to-Period converter topology based on re-laxation oscillators, which can get Resistance-to-Period converters with an accu-racy in the order of_±1%without complex calibration processes. The main goal is to provide robustness to voltage and temperature variations without the addition of auxiliary high-performance circuits or elements. A classic Resistance-to-Period converter based on a Resistance-to-Current converter and a Current-to-Period converter is first analyzed in order to show the main error sources and how they can be reduced. Based on the analysis, solutions to overcome the dependency on high-performance blocks to achieve better linearity, accuracy, and robustness without increasing complexity are proposed. Finally, at the end of the chapter, experimental results of several possible implementations of the proposed topology are shown.

3.1 Resistance to Period Converters: Building

Blocks

In general, a Resistance-to-Period Converter (RTC) where the resistor is isolated from the oscillator circuit can be built with two different blocks connected in se-ries: a Resistance-to-Current/Voltage converter and a Current/Voltage controlled oscillator, as shown in Figure 3.1. In the first case (Figure 3.1a) a constant volt-age (Vbias) biases the sensor and produces a current which is driven to a Current

Controlled Oscillator (CCO). In the second case, biasing is provided by a con-stant currentIbias (Figure 3.1b) and the voltage across the resistor is the output

parameter to be driven to a Voltage controlled oscillator (VCO). In both cases, the output frequency of the signal is a function of the resistanceRS.

CMOS interface circuits for metal-oxide gas sensors

CMOS interface circuits for

metal-oxide gas sensors

by

Luis Carlos ´

Alvarez Sim´on

A dissertation submitted to the Electronics

Department in partial fulfillment of the

requirements for the degree of

D.Sc. in Electronics

at the

National Institute for Astrophysics, Optics

and Electronics

December 2014

Tonantzintla, Puebla

Advisor:

Dra. Mar´ıa Teresa Sanz Pascual

c

INAOE 2014

All rights reserved

The author hereby grants to INAOE permission

to reproduce and to distribute copies of this

CMOS interface circuits for

metal-oxide gas sensors

by

M.C. Luis Carlos Álvarez Simón

Supervised by:

Dra. María Teresa Sanz Pascual

Abstract

Resumen

Agradecimientos

Contents

List of Figures

List of Tables

List of Acronyms

Chapter 1

Introduction

1.1

Previous work on interface circuits

+

..

.

1.1.1

Read-out circuits

1.1.2

Temperature control circuits

1.2

Objectives

1.3

Thesis Organization

Chapter 2

MOX gas sensors

2.1

Gas sensing principle

2.1.1

Sensing layer resistance

2.2

Gas sensing parameters

2.3

Ways to improve performance

2.4

Carbon monoxide MOX sensor

2.4.1

Experimental characterization of the heater

2.4.2

Experimental characterization of the sensing

resistance

2.5

Conclusions

Chapter 3

Read-out circuits

3.1

Resistance to Period Converters: Building

Blocks