A Low Voltage, Low Power 4 th Order Continuous-time Butterworth Filter for Electroencephalography Signal Recognition.
THESIS
Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University
By
Ridwan Saleh Mulyana. B.S.E.C.E.
Graduate Program in Electrical and Computer Engineering
The Ohio State University
2010
Master’s Examination Committee:
Professor Mohammed Ismail El-Naggar, Advisor
Professor Waleed Khalil
Copyright by
Ridwan Saleh Mulyana
2010
Abstract
Operational Transconductance Amplifier filter (OTA-C) in biomedical devices have been explored significantly because of its advantages in low power and low voltage design which is very important for battery powered biomedical devices. Main idea of this design is to implement Gm-C filter in low voltage, power, and frequency with increased linearity and dynamic range. Moreover, portable patient monitoring system devices becomes more significant because it does not limit one’s mobility in daily activities. In order to operate in sub-hertz frequency, a very low transconductance or a very high capacitance value is required. Increasing capacitor size is not an option as it will consume more silicon area.
In this thesis, a filter for Electroencephalography (EEG) data acquisition system is designed. It is based on continuous-time analog filter topology to provide a good accuracy and high performance. The designed circuit combines active linearization technique with series-parallel current array to achieve good linearity and low power consumption. A tunable fourth-order maximally flat continuous-time filter is designed by using the OTAs. By utilizing biquad filter structure which is implemented with several different OTA configurations, a higher order filter can be achieved simply by cascading its structure. ii
The overall system is designed in 180nm CMOS technology from TSMC library. In deep analysis of circuit and design procedures are discussed in the following chapters.
Simulation results are also included to verify the robustness of the design. Finally, with a supply voltage of 1.5V, the filter is capable of handling input signals up to 400mV with a low distortion (THD = 52.3dB). As the total active area only 750µm x 550µm and total power consumption of 4.7µW, the filter is suitable for portable EEG data acquisition system.
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Dedication
This document is dedicated to my family.
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Acknowledgments
First, I would like to thank my family for supporting me to study in electrical engineering field. Without them, I would never reach up to this point.
I also would like to thank my advisor, Professor Mohammed Ismail, for guiding me with knowledge and advice to go through this master program at the Ohio State University. His expertise and encouragement are very valuable to understand the big picture of why one supposes to do research. Additionally, I also thank to Professor Chung-Chih Hung and AIC group in National Chiao Tung University. Their outstanding supports in my electronics knowledge during the internship in Taiwan are extraordinary.
I would like to thank Professor Waleed Khalil for serving as master’s examination committee and providing valuable advice for my research. And thank to all friends in VLSI lab for supporting me during the school years. Finally, I would like to thank all my colleagues in
Industrial Engineering Department for supporting me to go through my study at the Ohio State
University.
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Vita
June, 2007………………………………………………….. B.S. Electrical and Computer Engineering, The Ohio State University.
July, 2007 – Present …………………………………… Graduate Associate Assistant,
The Ohio State University.
Fields of Study:
Major Field: Electrical and Computer Engineering
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Table of Contents
Abstract ...... ii
Dedication ...... iv
Acknowledgments...... v
Vita ...... vi
Fields of Study: ...... vi
Table of Contents ...... vii
List of Figures ...... x
List Of Tables ...... xiii
Chapter 1 : Introduction...... 1
1.1 Background ...... 1
1.2 Basic Structure of Biomedical Data Acquisition ...... 1
1.2.1 Electroencephalography Data Acquisition system...... 2
1.3 Analog Active Filter ...... 4
1.4 Thesis Organization ...... 5
Chapter 2 : Differential Amplifiers ...... 7 vii
2.1 Introduction ...... 7
2.2 General Methods of Improving Linearity ...... 8
2.2.1 Differential structure ...... 8
2.2.2 Constant Drain-Source Structure ...... 10
2.2.3 Source Degeneration Structure ...... 11
2.2.4 Pseudo-Differential Structure ...... 13
2.3 Advanced Models of Improving Linearity ...... 15
2.3.1 Multiple Differential Pair Structures ...... 16
2.3.2 Constant Drain Source Voltage with Opamps ...... 16
Chapter 3 : Low Voltage Low Power OTAs Architecture...... 18
3.1 Weak Inversion Transconductor ...... 18
3.2 A Weak Inversion OTA with active source degeneration transistors and Current
Division Array...... 20
3.3 Common Mode Sensing Circuit ...... 23
Chapter 4 : OTA-C Filters ...... 26
4.1 Introduction ...... 26
4.2 Transconductor Elements ...... 27
4.2.1 Resistors ...... 27
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4.2.2 Amplifiers ...... 28
4.2.3 Gyrators ...... 29
4.2.4 Integrators ...... 31
4.3 Fourth Order Maximally Flat Low-Pass Filter ...... 34
4.3.1 2 nd Order Biquad Block Structure ...... 34
4.3.2 Low-Pass Filter Implementation ...... 37
4.3.3 Extra Circuitry ...... 40
Chapter 5 : Simulation Results and Discussion of OTA-C Filter ...... 42
5.1 Parameters ...... 42
5.2 Performance of Designed OTAs and Maximally Flat Filter ...... 45
5.2.1 Pre-Simulation results of Transconductor and Filter ...... 45
5.2.2 Post-Simulation results of Transconductor and Filter ...... 52
Chapter 6 : Conclusion and Future Work ...... 61
6.1 Conclusion ...... 61
6.2 Future Work ...... 61
References: ...... 63
ix
List of Figures
Figure 1.1. General diagram of data acquisition...... 1
Figure 1.2. EEG circuitry block diagram...... 3
Figure 1.3. Filter type based on the operating frequency range [18]...... 5
Figure 2.1. Differential input pair...... 8
Figure 2.2. Constant drain-source voltage structure...... 10
Figure 2.3. Source degeneration technique...... 12
Figure 2.4. NMOS and PMOS Pseudo Differential Input Pairs...... 14
Figure 2.5. Pseudo differential with common mode feedforward...... 15
Figure 2.6. Multiple Input Differential Input Pair...... 16
Figure 2.7. Linearity improved by the use of op-amp...... 17
Figure 3.1. Open loop gain vs. bias current [5]...... 20
Figure 3.2. Series-parallel current array...... 21
Figure 3.3. OTA with integrated CMFB...... 21
Figure 3.4. Common Mode Circuit...... 24
Figure 3.5. Resistor-based CMFB...... 24
Figure 3.6. Transistos-based CMFB...... 25
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Figure 4.1. Resistor-like configurations of transconductors: (a) grounded single ended output. (b) differential. (c) floating resistor...... 28
Figure 4.2. Single-ended OTAs amplifier...... 29
Figure 4.3. Single-ended output Gyrator...... 30
Figure 4.4. Floating inductor...... 31
Figure 4.5. Integrators. (a) single-ended. (b) fully differential with floating capacitor. (c)
fully differential with grounded capacitors. (d) non-ideal integrator...... 32
Figure 4.6. (a) RLC equivalent circuit. (b) Norton’s equivalent circuit...... 35
Figure 4.7. 2nd Order single ended and fully differential biquad structure...... 36
Figure 4.8. A 4th Order Maximally Flat Low-Pass Biquad Filter...... 38
Figure 4.9. (a) Extra circuit blocks. (b) Biasing circuit. (c) Output buffer...... 41
Figure 5.1. OTA frequency response at typical typical (TT)...... 46
Figure 5.2.OTA Frequency response at fast fast (FF) corner...... 46
Figure 5.3.OTA frequency response at slow slow (SS) corner...... 47
Figure 5.4. Gm variation in some input range...... 47
Figure 5.5. Tranconductance values at different tuning voltage of source degenerated
resistance...... 48
Figure 5.6. Common mode rejection ratio at different corner...... 49
Figure 5.7. Power supply rejection ratio at different corner...... 49
Figure 5.8. Common Mode Transient Response...... 50
Figure 5.9. Filter frequency response at TT, FF, and SS corner...... 50
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Figure 5.10. Filter HD3 analysis at 40Hz 0.4Vpp input signal...... 51
Figure 5.11. Filter tuning range...... 52
Figure 5.12. OTA frequency response at TT corner...... 53
Figure 5.13. OTA frequency response at SS corner...... 53
Figure 5.14. OTA frequency response at FF corner...... 54
Figure 5.15. Post-sim CMRR at different corner...... 54
Figure 5.16. Post-sim PSRR at different corner...... 55
5.17. Gm in montecarlo analysis...... 56
5.18. Gain and phase in Montecarlo analysis...... 56
5.19. CMRR in Montecarlo analysis...... 57
5.20. PSRR in Montecarlo analysis...... 57
Figure 5.21.Post-sim filter response at different corner...... 58
Figure 5.22. Post-sim filter HD3 at 40Hz 0.4Vpp input signal...... 58
Figure 5.23. Chip photo...... 59
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List of Tables
Table 1. Brain waves classification [1]...... 3
Table 2. OTA and filter specification...... 60
Table 3. Literature Comparison...... 60
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Chapter 1 : Introduction
1.1 Background
Biomedical electronics and devices have been growing promptly in the modern time. The requirements of low power and low voltage circuit design have become major research topics because of limited amount of silicon area and energy sources. In some biomedical devices, for example: pacemaker and hearing aid, portability and compact design are the major points of the design goals. Human biological signals operate in sub-hertz to several kilo hertz with amplitude in the range of micro volts [1]. To record these type of signals need complex and high performance filters to achieve accurate data in such noisy situation.
1.2 Basic Structure of Biomedical Data Acquisition
Figure 1.1. General diagram of data acquisition.
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In figure 1.1, it describes a general circuit diagram of biomedical data
acquisition device. It starts by connecting human body signals by using highly
conductive silver electrodes coated with silver-chloride and gold cup electrodes to
the front end transducer. Next, the analog block amplifies these signals up to the
desired operating point and an analog filter cleans out the unnecessary noise that
carries along with these signals. An Analog-Digital Converter (ADC) picks up the
analog signals and translates them into string of 1 and 0. A Digital Signal Processing
(DSP) processes the bits in digital domain and the results can be stored in various
digital media storages, e.g., hard disk, flash memory, writable disc, and etc.
1.2.1 Electroencephalography Data Acquisition system.
Electroencephalography, commonly known as EEG, is a method to measure brain waves by recording the electrical movements along the head scalp by the firing of neurons within the brain [2]. The electrical activity is recorded through highly conductive electrodes attached to the head’s surface and sends the signals into EEG machine. The recording can be done in several different conditions depending on the objective. Most of the time, EEG is used in medical practice to diagnose epilepsy, brain death, coma, and some other abnormal activities in brain. In the early stage,
EEG was used to detect brain tumors and focal brain disorders [3].
Several different rhythmic oscillations can be classified to define brain activities. Five major brain waves (Alpha, Beta, Delta, Gamma, and Theta waves)
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define most the brain activities with three additional minor bands (Kappa, Lambda, and Mu waves) as the complements [1]. The classification is based on the operating frequency range of each wave and the state of the sample, in this case, human biological sample. The following table shows the characteristic of major and minor bands:
Band Frequency (Hz) Amplitude ( Individuals State Alpha 8 – 13 20 - 60 ) Relaxed, closed eyes Beta 13 – 40 2 – 20 Excited mental/ physical Delta 0.5 – 3.5 20 – 200 Deep sleep normal person Theta 4 – 7 20 - 100 Drowsiness in young adults Gamma 36 – 44 3 - 5 Sensory stimuli Kappa 10 N/A Thinking Lambda N/A 20-50 Visual image Mu 8-13 N/A Sensori motor cortex Table 1. Brain waves classification [1].
Figure 1.2. EEG circuitry block diagram.
EEG data acquisition system, as shown in figure above, detects the electrical signals by using several conductive electrodes attached to head scalp. The EEG machine receives signals from electrodes and gets amplify by an instrumental
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amplifier. The amplification has to be done because signals from electrodes are too weak to be processed. Once the signals are properly amplified, a low pass filter determines the cutoff frequency of each brain wave. In this case, a tunable low pass filter is favorable. It plays an important role in the system because noise, which has been amplify by the instrument amplifier, needs to be canceled out in order to achieve high accuracy of the brain signals. Finally, the filtered signal goes the digital block which processes the signal in digital domain. It is necessary to have an extra analog circuitry, a Driven Right Leg (DRL) circuit, which feeds back into the biological sample to cancel out common mode interference.
1.3 Active Filter
In general, active filter design divides into two, digital and analog filters.
Analog filter processes the signal continuously, while the digital filter converts the signal into digital domain and processes in that mode. Based on the operating region, filters are classified into LC filter, Integrated Gm-C filter, Switched-Capacitor,
Active R-C, and waveguide filter (shown in figure 1.3). In Biomedical electronics, high performance low frequency filter is needed and switched capacitor filter is commonly implemented. However, it consumes quiet amount of power and is vulnerable to clock jitter, which generates noise that would affect the main signal [4].
On the other hand, Integrated Gm-C filter has a wide operating range, which can be suited from low to high frequency applications. It operates in open loop
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architecture meaning that the performance of the system depends on the performan ce of the transconductor. Since Gm-C filter processes the analog signal continuously; with a proper design and tuning values, the total noise and the power consumption will be lower than switched capacitor topology. The remaining challenge is to increase Gm -C cell performance to adjust with the present requirements. Th erefore, implementing Gm -C filter in low power, low voltage, and low frequency applications have been researched in details.
Figure 1.3. Filter type based on the operating frequency range [18].
1.4 Thesis O rganization
In the next few sections, some important design techniques for filters are covered. Chapter 2 describes high linearity structures of Operational
Transconductance Amplifiers (OTA). It explains the characteristics, superiorities as well as the disadvantages of each different model. Chapter 3 covers the proposed
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design of OTA, which shows a high performance, low operating frequency range, and very low power consumption structure. Circuit analyses, including: design concept and mathematical formulations will be discussed to verify the structure. Chapter 4 explains a principle of designing OTA-C or known as Gm-C filters, which includes other supporting basic blocks that supports the filter operation. Chapter 5 discusses the simulations procedures and results of the Gm-C filter and corresponding test bench. Chapter 6 wraps up the thesis with summary and conclusion.
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Chapter 2 : Differential Amplifiers
2.1 Introduction
Transconductance is one of the most essential parameter in designing an analog circuit. It is usually represented as g m in AC small signal analysis. An
Operational Tranconductance Amplifier (OTA) is a voltage controlled current source type of amplifier because of its proportionality between the input voltage and the output current. A difference of an OTA from Op-Amp is that the all of the nodes, except input and output nodes, have low impedances [5]. A relation between transconductance (gm), AC gate voltage, and drain current can be described as: