DOCSLIB.ORG

APPLICATION BULLETIN Mailing Address: PO Box 11400 • Tucson, AZ 85734 • Street Address: 6730 S

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
APPLICATION BULLETIN Mailing Address: PO Box 11400 • Tucson, AZ 85734 • Street Address: 6730 S ®APPLICATION BULLETIN Mailing Address: PO Box 11400 • Tucson, AZ 85734 • Street Address: 6730 S. Tucson Blvd. • Tucson, AZ 85706 Tel: (602) 746-1111 • Twx: 910-952-111 • Telex: 066-6491 • FAX (602) 889-1510 • Immediate Product Info: (800) 548-6132 FAST SETTLING LOW-PASS FILTER By Rod Burt and R. Mark Stitt (602) 746-7445 Noise reduction by filtering is the most commonly used V method for improving signal-to-noise ratio. The increase in IN 1 settling time, however, can be a serious disadvantage in some applications such as high-speed data acquisition sys- R1 tems. The nonlinear filter described here is a simple way to 10kΩ 2 get a four-to-one improvement in settling time as compared VOUT to a conventional filter. C1* 3 To understand the circuit, first consider the dynamics of a single-pole RC filter (Figure 1). Filtering reduces broadband or “white” noise by the square root of the bandwidth reduc- * For 10kHz, C1 = 1592pF. tion as shown by the following calculation: f = 1/(2 • π • R • C ) 4 –3dB 1 1 f f 2 2 FIGURE 1. Conventional Single-Pole RC Filter. e 2 = e 2 df = e 2 • f n B B f f 1 1 VIN 1/2 en = eB (f2 – f1) Diodes Where: R1 10kΩ 1N4148 en = total noise (Vrms) e = broadband noise (V/√Hz) 6 B VOUT f1, f2 = frequency range of interest (Hz) C1* In other words, if the frequency range (f2 – f1) is reduced by a factor of 100, the total noise would be reduced by a factor 7 of 10. * For 10kHz, C1 = 1592pF. π Unfortunately, settling time depends on bandwidth. The f–3dB = 1/(2 • • R1 • C1) penalty for the noise reduction is increased settling time. For 8 a single-pole filter, the time needed for the signal to settle to FIGURE 2. Diode-Clamped Nonlinear Filter (can improve any given accuracy can be calculated as follows: 0.01% settling time for a conventional filter by 9 2/1 for a 20V step). For VO/VIN at time = tS VO = 1 – e–(tS/[R1 • C1]) NONLINEAR FILTER V IN To understand how a nonlinear filter can improve settling 10 time, consider the simple diode clamped nonlinear filter, VO – ( – 1) • 100 = % shown in Figure 2. Settling time is improved because the VIN 11 filter capacitor, C1, is charged faster through the low forward therefore biased diode impedance (RON) during the initial portion of a tS = –ln(%/100) • R1 • C1 large input step change. When the difference between the 12 Where: input and output voltage becomes less than the forward biased diode drop (about 0.6V), the diode turns off and C t = settling time (s) 1 S 13 reacts with R1 alone. At this point, the circuit behaves like a % = percent accuracy at tS Ω normal single-pole RC filter. R1 • C1 = RC time constant ( • F) or (s) Assuming diode R is negligible, the improvement in 14 For example, if a settling to 0.01% is needed, ON settling time depends on the ratio of the input step voltage to ln(0.01/100) = –9.2 the forward biased diode voltage. For a step of –10V to +10V (a 20V step), the improvement is ln(0.60/20) or 3.5 15 In other words, it takes 9.2 R1 • C1 time constants for an input step to settle to within 0.01% of its final value. time constants. In other words, for a 20V step, the simple 16 ©1991 Burr-Brown Corporation AB-022 Printed in U.S.A. January, 1991 SBOA011 R VIN 4 Ω VIN 10k OPA627 OPA627 C R2 3 300kΩ (C1/10) Diodes C2 1N4148 R1 10kΩ (C1/50) R3 100Ω VOUT C1* R2 300kΩ Diodes 1N4148 * For 10kHz, C1 = 1592pF. R f = 1/(2 • π • R • C ) 1 –3dB 1 1 10kΩ V FIGURE 3. Improved Nonlinear Filter (can improve 0.01% OUT settling time for a conventinal filter by 4/1 for C1* a 20V step). * For 10kHz, C1 = 1592pF. diode clamped nonlinear filter can improve 0.01% settling π f–3dB = 1/(2 • • R1 • C1) time from 9.2 time constants to (9.2) – (3.5) = 5.7 time constants. FIGURE 4. Improved Nonlinear Filter with R4, C3 Prefilter For smaller differences between the input step change, and and R3, C2 Network to Assure Op Amp Stability the forward biased diode voltage, the simple diode-clamped in Driving C1. nonlinear filter affords less improvement. As the step change approaches the forward biased diode voltage, the simple threshold of ten times this peak (20mV) is an arbitrary but nonlinear filter offers no improvement. ample threshold. The component values shown in Figure 3 set the filter’s threshold to 20mV. For a 20V step as before, IMPROVED NONLINEAR FILTER the improvement in settling time is ln(0.02/20) = 6.9 time By reducing the threshold to below one diode drop, the constants. In other words, for a 20V step, the improved settling time can be improved for smaller inputs. The im- nonlinear filter can improve 0.01% settling time from 9.2 proved nonlinear filter shown in Figure 3 lets you adjust the time constants to (9.2) – (6.9) = 2.3 time constants—a four- threshold to a small arbitrary value by adjusting the ratio of to-one improvement. R1 and R2. SOME SIGNALS REQUIRE PREFILTERING To see how the improved nonlinear filter works, notice that the op amp forces the voltage at its inverting input to be the In some instances, the input signal may have noise peaks same as at the noninverting input. For small differences above the 20mV threshold of the nonlinear filter. If the noise between the output voltage and the input voltage, the differ- of the input signal to the nonlinear filter is greater than the ence is dropped across the 10kΩ resistor, R , and the filter 20mV threshold, the filter will mistake the noise for a step 1 input and fail to filter it out. To prevent this situation an behaves like a single-pole filter with an R1 • C1 time con- stant. The voltage divider formed by R and R amplifies the input prefilter can be added to the nonlinear filter as shown 1 2 in Figure 4. voltage difference across R1, as seen at the top of R2, by (1 + R /R ). As the voltage across the R , R divider becomes 2 1 1 2 The prefilter’s bandwidth is set by R4 and C3. To minimize larger, one of the diodes (which one depends on signal the prefilter’s effect on settling time, its bandwidth is set ten polarity) begins to conduct, and the capacitor is rapidly times higher than the bandwidth of the nonlinear filter. At charged through it. This occurs at a voltage difference this higher bandwidth, the prefilter’s effect on settling time √ between the input and output of about 0.6V/(1 + R2/R1) or is negligible, and the noise at the prefilter’s output is 10 about 20mV with the values shown. With the diode forward times greater than 2mV (a little over 6mV peak). A noise biased, the time constant of the filter becomes very small, level of 6mV provides a comfortable margin for a 20mV limited only by op amp slew rate or current limit. threshold. To determine the component values for the improved non- linear filter, consider the noise-reduction requirements of the ASSURE OP AMP STABILITY filter. For example, if you want to filter the noise of a 20V Op amps tend to become unstable and oscillate when driving full-scale signal to 0.01% resolution, the peak noise must be large capacitive loads. The C2, R3 network, shown in Figure filtered to less than 0.01% of 20V, i.e. 2mV peak. A clamp 4, assures op amp stability when driving large values of C1 2 ± Traces 1 to 3 show settling response for the various filters. Gain of the Trace 1 shows 10kHz single-pole filter response for a 10mV input signals is 100V/V, so each division of the scope graticule represents step. 2mV (i.e. each division = 0.01%). Trace 2 shows fast response for a ±10V input step. Trace 1 is a standard 10kHz single-pole RC filter. Trace 2 is a diode-clamped 10kHz nonlinear filter. FIGURE 6. Comparison of Large and Small Signal Step Trace 3 is the improved 10kHz nonlinear filter shown in Figure 4. Response of the Improved Nonlinear Filter. Trace 4 is the ±10V input signal at 5V/division. THEORETICAL THEORETICAL FIGURE 5. Settling-Time Response of Standard and Non- FILTER SETTLING TIME SETTLING TIME µ (1) linear Filters. TYPE (time constants) ( s) Single-Pole RC 9.2 147 through the low impedance of the forward biased diode Diode-Clamped 5.7 91 network. The C , R network may not be needed if C is Nonlinear 2 3 1 Improved 2.3 37 small. Nonlinear When choosing the op amp for the improved filter, make NOTE: (1) Settling to 0.01% of final value for a 10kHz filter. sure it has high output drive capability for charging C1, and that its slew rate, settling time, and DC precision are ad- TABLE I. Theoretical Settling Times for Various Filters. equate for the necessary filter response. The OPA627 shown has an excellent combination of DC precision, high slew of the base grid. rate, fast settling time and high output drive capability. The For a 10kHz filter, one RC time constant is 15.9µs. Ignoring 16MHz bandwidth of the OPA627 gives excellent results for input slew rate, and diode forward resistance (good approxi- filters with –3dB bandwidths up to 100kHz.
Load more

Recommended publications
  • Linear Filtering of Random Processes
    Linear Filtering of Random Processes Lecture 13 Spring 2002 Wide-Sense Stationary A stochastic process X(t) is wss if its mean is constant E[X(t)] = µ and its autocorrelation depends only on τ = t1 − t2 ∗ Rxx(t1,t2)=E[X(t1)X (t2)] ∗ E[X(t + τ)X (t)] = Rxx(τ) ∗ Note that Rxx(−τ)=Rxx(τ)and Rxx(0) = E[|X(t)|2] Lecture 13 1 Example We found that the random telegraph signal has the autocorrelation function −c|τ| Rxx(τ)=e We can use the autocorrelation function to find the second moment of linear combinations such as Y (t)=aX(t)+bX(t − t0). 2 2 Ryy(0) = E[Y (t)] = E[(aX(t)+bX(t − t0)) ] 2 2 2 2 = a E[X (t)] + 2abE[X(t)X(t − t0)] + b E[X (t − t0)] 2 2 = a Rxx(0) + 2abRxx(t0)+b Rxx(0) 2 2 =(a + b )Rxx(0) + 2abRxx(t0) −ct =(a2 + b2)Rxx(0) + 2abe 0 Lecture 13 2 Example (continued) We can also compute the autocorrelation Ryy(τ)forτ =0. ∗ Ryy(τ)=E[Y (t + τ)Y (t)] = E[(aX(t + τ)+bX(t + τ − t0))(aX(t)+bX(t − t0))] 2 = a E[X(t + τ)X(t)] + abE[X(t + τ)X(t − t0)] 2 + abE[X(t + τ − t0)X(t)] + b E[X(t + τ − t0)X(t − t0)] 2 2 = a Rxx(τ)+abRxx(τ + t0)+abRxx(τ − t0)+b Rxx(τ) 2 2 =(a + b )Rxx(τ)+abRxx(τ + t0)+abRxx(τ − t0) Lecture 13 3 Linear Filtering of Random Processes The above example combines weighted values of X(t)andX(t − t0) to form Y (t).
    [Show full text]
  • Chapter 3 FILTERS
    Chapter 3 FILTERS Most images are a®ected to some extent by noise, that is unexplained variation in data: disturbances in image intensity which are either uninterpretable or not of interest. Image analysis is often simpli¯ed if this noise can be ¯ltered out. In an analogous way ¯lters are used in chemistry to free liquids from suspended impurities by passing them through a layer of sand or charcoal. Engineers working in signal processing have extended the meaning of the term ¯lter to include operations which accentuate features of interest in data. Employing this broader de¯nition, image ¯lters may be used to emphasise edges | that is, boundaries between objects or parts of objects in images. Filters provide an aid to visual interpretation of images, and can also be used as a precursor to further digital processing, such as segmentation (chapter 4). Most of the methods considered in chapter 2 operated on each pixel separately. Filters change a pixel's value taking into account the values of neighbouring pixels too. They may either be applied directly to recorded images, such as those in chapter 1, or after transformation of pixel values as discussed in chapter 2. To take a simple example, Figs 3.1(b){(d) show the results of applying three ¯lters to the cashmere ¯bres image, which has been redisplayed in Fig 3.1(a). ² Fig 3.1(b) is a display of the output from a 5 £ 5 moving average ¯lter. Each pixel has been replaced by the average of pixel values in a 5 £ 5 square, or window centred on that pixel.
    [Show full text]
  • A Multidimensional Filtering Framework with Applications to Local Structure Analysis and Image Enhancement
    Linkoping¨ Studies in Science and Technology Dissertation No. 1171 A Multidimensional Filtering Framework with Applications to Local Structure Analysis and Image Enhancement Bjorn¨ Svensson Department of Biomedical Engineering Linkopings¨ universitet SE-581 85 Linkoping,¨ Sweden http://www.imt.liu.se Linkoping,¨ April 2008 A Multidimensional Filtering Framework with Applications to Local Structure Analysis and Image Enhancement c 2008 Bjorn¨ Svensson Department of Biomedical Engineering Linkopings¨ universitet SE-581 85 Linkoping,¨ Sweden ISBN 978-91-7393-943-0 ISSN 0345-7524 Printed in Linkoping,¨ Sweden by LiU-Tryck 2008 Abstract Filtering is a fundamental operation in image science in general and in medical image science in particular. The most central applications are image enhancement, registration, segmentation and feature extraction. Even though these applications involve non-linear processing a majority of the methodologies available rely on initial estimates using linear filters. Linear filtering is a well established corner- stone of signal processing, which is reflected by the overwhelming amount of literature on finite impulse response filters and their design. Standard techniques for multidimensional filtering are computationally intense. This leads to either a long computation time or a performance loss caused by approximations made in order to increase the computational efficiency. This dis- sertation presents a framework for realization of efficient multidimensional filters. A weighted least squares design criterion ensures preservation of the performance and the two techniques called filter networks and sub-filter sequences significantly reduce the computational demand. A filter network is a realization of a set of filters, which are decomposed into a structure of sparse sub-filters each with a low number of coefficients.
    [Show full text]
  • Signal Processing with Scipy: Linear Filters
    PyData CookBook 1 Signal Processing with SciPy: Linear Filters Warren Weckesser∗ F Abstract—The SciPy library is one of the core packages of the PyData stack. focus on a specific subset of the capabilities of of this sub- It includes modules for statistics, optimization, interpolation, integration, linear package: the design and analysis of linear filters for discrete- algebra, Fourier transforms, signal and image processing, ODE solvers, special time signals. These filters are commonly used in digital signal functions, sparse matrices, and more. In this chapter, we demonstrate many processing (DSP) for audio signals and other time series data of the tools provided by the signal subpackage of the SciPy library for the design and analysis of linear filters for discrete-time signals, including filter with a fixed sample rate. representation, frequency response computation and optimal FIR filter design. The chapter is split into two main parts, covering the two broad categories of linear filters: infinite impulse response Index Terms—algorithms, signal processing, IIR filter, FIR filter (IIR) filters and finite impulse response (FIR) filters. Note. We will display some code samples as transcripts CONTENTS from the standard Python interactive shell. In each interactive Python session, we will have executed the following without Introduction....................1 showing it: IIR filters in scipy.signal ..........1 IIR filter representation.........2 >>> import numpy as np >>> import matplotlib.pyplot as plt Lowpass filter..............3 >>> np.set_printoptions(precision=3, linewidth=50) Initializing a lowpass filter.......5 Bandpass filter..............5 Also, when we show the creation of plots in the transcripts, we Filtering a long signal in batches....6 won’t show all the other plot commands (setting labels, grid Solving linear recurrence relations...6 lines, etc.) that were actually used to create the corresponding FIR filters in scipy.signal ..........7 figure in the paper.
    [Show full text]
  • Filters CW100322-01
    PHY3128 Electronics for Measurement Systems Linear Filters Linear Filters Introduction A linear filter is a circuit with an output Vout that is a linear function of its input Vin and that is intended to remove unwanted parts of a signal. Such filters are often described by specifying how they modify the amplitude and phase of each frequency component of the signal with a transfer function € € 2 Vout (ω) a0 + a1ω + a2ω + ... G(ω) = = 2 . (1) Vin (ω) b0 + b1ω + b2ω + ... This quotient of two polynomials is known as a Padé function and is usually expressed as the product of its complex poles and zeros, e.g. € (ωz0 + jω) × (ωz1 + jω) × ... G(ω) = (2) (ωp0 + jω) × (ωp1 + jω) × ... Much cryptic engineering-jargon arises from this convention. For example a ‘single-pole op-amp’ is one with an open-loop gain accurately described by € a A(ω ) = 0 (3) ω 0 + jω which has a single complex pole at ω = jω0 . In principle, designing a filter is a straightforward matter – one uses a computer and/or creates a chain of stages until the desired transfer function is obtained. However, non-trivial designs often do not work well; they tend to require extremely precise component values that are not available in practice. There are many of types of filter; the most commonly encountered include: Butterworth (maximum flatness in pass-band), Chebyshev (sharpest transition from pass-band to stop-band), Bessel (best linear phase vs frequency response). General Remarks In the context of test and measurement systems, filters irreversibly remove information from the signal and should be used sparingly, particularly if it would be possible to post-process signals digitally.
    [Show full text]
  • Convention Paper 7997 Presented at the 128Th Convention 2010 May 22–25 London, UK
    Audio Engineering Society Convention Paper 7997 Presented at the 128th Convention 2010 May 22–25 London, UK The papers at this Convention have been selected on the basis of a submitted abstract and extended precis that have been peer reviewed by at least two qualified anonymous reviewers. This convention paper has been reproduced from the author’s advance manuscript, without editing, corrections, or consideration by the Review Board. The AES takes no responsibility for the contents. Additional papers may be obtained by sending request and remittance to Audio nd Engineering Society, 60 East 42 Street, New York, New York 10165-2520, USA; also see www.aes.org. All rights reserved. Reproduction of this paper, or any portion thereof, is not permitted without direct permission from the Journal of the Audio Engineering Society. Modeling Distortion Effects in Class-D 8094 Amplifier Filter Inductors Arnold Knott1, Tore Stegenborg-Andersen 1, Ole C. Thomsen 1, Dominik Bortis 2, Johann W. Kolar 2 Gerhard Pfaffinger3 and Michael A.E. Andersen1 1Technical University of Denmark, 2800 Kgs. Lyngby, Denmark 2ETH Zurich / Power Electronics System Laboratory, CH-8092 Zurich, Switzerland 3Harman/Becker Automotive Systems GmbH, 94315 Straubing, Germany Correspondence should be addressed to Arnold Knott ([email protected]) ABSTRACT Distortion is generally accepted as a quantifier to judge the quality of audio power amplifiers. In switch- mode power amplifiers various mechanisms influence this performance measure. After giving an overview of those, this paper focuses on the particular effect of the nonlinearity of the output filter components on the audio performance. While the physical reasons for both, the capacitor and the inductor induced distortion are given, the practical in depth demonstration is done for the inductor only.
    [Show full text]
  • The Scientist and Engineer's Guide to Digital Signal Processing
    CHAPTER 14 Introduction to Digital Filters Digital filters are used for two general purposes: (1) separation of signals that have been combined, and (2) restoration of signals that have been distorted in some way. Analog (electronic) filters can be used for these same tasks; however, digital filters can achieve far superior results. The most popular digital filters are described and compared in the next seven chapters. This introductory chapter describes the parameters you want to look for when learning about each of these filters. Filter Basics Digital filters are a very important part of DSP. In fact, their extraordinary performance is one of the key reasons that DSP has become so popular. As mentioned in the introduction, filters have two uses: signal separation and signal restoration. Signal separation is needed when a signal has been contaminated with interference, noise, or other signals. For example, imagine a device for measuring the electrical activity of a baby's heart (EKG) while still in the womb. The raw signal will likely be corrupted by the breathing and heartbeat of the mother. A filter might be used to separate these signals so that they can be individually analyzed. Signal restoration is used when a signal has been distorted in some way. For example, an audio recording made with poor equipment may be filtered to better represent the sound as it actually occurred. Another example is the deblurring of an image acquired with an improperly focused lens, or a shaky camera. These problems can be attacked with either analog or digital filters. Which is better? Analog filters are cheap, fast, and have a large dynamic range in both amplitude and frequency.
    [Show full text]
  • An ON–OFF Log Domain Circuit That Recreates Adaptive Filtering in the Retina Kareem A
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I: REGULAR PAPERS, VOL. 52, NO. 1, JANUARY 2005 99 An ON–OFF Log Domain Circuit That Recreates Adaptive Filtering in the Retina Kareem A. Zaghloul and Kwabena A. Boahen Abstract—We introduce a new approach to synthesizing Class fore, to obtain the derivative of the voltage, divide the derivative AB log-domain filters that satisfy dynamic differential-mode of the signal, , by the signal, . That is to say, divide the and common-mode constraints simultaneously. Whereas the current you wish to supply to the capacitor by the current made dynamic differential-mode constraint imposes the desired fil- tering behavior, the dynamic common-mode constraint solves by the transistor whose gate (or base) is connected to it. Intu- the zero-dc-gain problem, a shortcoming of previous approaches. itively, this division compensates for the slope of the exponential Also, we introduce a novel push–pull circuit that serves as a at the transistor’s operating point, such that its current changes current-splitter; it rectifies a differential signal into the ON and at a constant rate. Current-division is readily realized with log- OFF paths in our log-domain filter. As an example, we synthesize a arithmic elements by exploiting the translinear principle [8]. first-order low-pass filter, and, to demonstrate the rejection of dc signals, we implement an adaptive filter by placing this low-pass In theory, log-domain filters have limitless dynamic range; in circuit in a variable-gain negative-feedback path. Feedback gain is practice, dynamic range is limited by the bias current.
    [Show full text]
  • Design of a 100 Mhz, 5 Order Elliptic, Low-Pass Switched Capacitor Filter
    1 Design of a 100 MHz, 5th Order Elliptic, Low-Pass Switched Capacitor Filter Jon Guerber, ECE 626, Student Member, IEEE TABLE I Abstract—The design and simulation of an Elliptic switched FILTER DESIGN SPECIFICATIONS capacitor filter with modeled non-idealities will be discussed and analyzed. The complete design procedure starting with Parameter Specified Value specifications will be shown. Non-idealities such as finite bandwidth, charge injection, slew rate, offset, finite gain and real world opamps have been incorporated into this study and their Sampling Frequency 100 MHz effect will be demonstrated though simulation. Finally dynamic DC Gain 0 dB range scaling and chip area scaling will be examined for optimal Passband Frequency 0-5 MHz design. Ripple in Passband < 0.2 dB I. INTRODUCTION Stopband Frequency 10-50 MHz ince the dawn of the electronics, the physical laws of Gain in Stopband < -50 dB Snature have necessitated a way to remove unwanted noise Minimum Capacitor Size 0.05 pF from a desired signal. The filter was created to mitigate this problem and has evolved from crude passive device configurations to highly modeled continuous and discrete time integrated blocks. Currently, many low frequency filters With use of Matlab filter design tools, we can quickly employ switched capacitor methods due to the high compare the relative order of the filter for the specifications predictability, reduced cost, and ease of design. above. These filter order results are shown in Table 2. In this paper, a switched capacitor filter with a sampling TABLE II frequency of 100 MHz has been designed. This filter is FILTER ORDER REQUIREMENTS designed without phase considerations, assuming that its use will be in delay tolerant systems or will be followed by an all- Filter Type Minimum Order pass phase correcting filter.
    [Show full text]
  • Filter Design Considerations for High Performance
    FILTER DESIGN CONSIDERATIONS FOR HIGH PERFORMANCE CONTINUOUS-TIME LOW-PASS SIGMA-DELTA ADC A Thesis by VENKATA VEERA SATYA SAIR GADDE Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE December 2009 Major Subject: Electrical Engineering FILTER DESIGN CONSIDERATIONS FOR HIGH PERFORMANCE CONTINUOUS-TIME LOW-PASS SIGMA-DELTA ADC A Thesis by VENKATA VEERA SATYA SAIR GADDE Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Approved by: Chair of Committee, Jose Silva-Martinez Committee Members, Shankar P. Bhattacharyya Aydin I. Karsilayan Jay Porter Head of Department, Costas N. Georghiades December 2009 Major Subject: Electrical Engineering iii ABSTRACT Filter Design Considerations for High Performance Continuous-time Low-pass Sigma- delta ADC. (December 2009) Venkata Veera Satya Sair Gadde, B. E., Birla Institute of Technology & Science, Pilani Chair of Advisory Committee: Dr. Jose Silva-Martinez Continuous-time filters are critical components in the implementation of large bandwidth, high frequency, and high resolution continuous-time (CT) sigma-delta (ΣΔ) analog-to-digital converters (ADCs). The loop filter defines the noise-transfer function (NTF) and hence the quantization noise-shaping behavior of the ΣΔ modulator, and becomes the most critical performance determining part in ΣΔ ADC. This thesis presents the design considerations for the loop filter in low-pass CT ΣΔ ADC with 12-bits resolution in 25MHz bandwidth and low power consumption using 0.18μm CMOS technology. Continuous-time filters are more suitable than discrete-time filters due to relaxed amplifier bandwidth requirements for high frequency ΣΔ ADCs.
    [Show full text]
  • Chapter 3 Linear Filters
    Chapter 3 Linear Filters Mikael Olofsson — 2003–2007 A signal is a - normally time-varying - measure of some kind, and filters are devices that manipulate signals. We use mathematical descriptions to model our filters and signals. It should be emphasized that those mathematical descriptions are precisely that - models. How well such a model describes reality has to do with how detailed the model is, what frequencies we are interested in, or in what way input signals are limited. The same model can be good in one situation and bad in another, by which we mean how well it predicts the outcome of an experiment. Linear filters belong to the class of linear systems. However, before we can say anything about systems, we need to start our presentation with signals as functions of time. After that, we continue to describe systems and their properties in the time domain, followed by both signals and systems described in terms of frequencies. Last, standard characterizations of filters are given. 3.1 Signals First of all, a signal is represented as a function of time, where we usually let t denote time. In the practical situation, signals can be almost any physical measures, such as currents, voltages, preassures, positions, and so forth. In telecommunication systems, however, most often we are dealing with voltages and currents, but also electromagnetic phenomena like radio waves and light. Mostly, we are interested in signals that represent some information. In some situations, e.g. when recording sound, the signal can be that information in itself. In other situations, a signal can represent some particular information, e.g.
    [Show full text]
  • CMOS Analog Filter Design for Very High Frequency Applications
    electronics Article CMOS Analog Filter Design for Very High Frequency Applications Luis Abraham Sánchez-Gaspariano 1,*, Carlos Muñiz-Montero 2, Jesús Manuel Muñoz-Pacheco 1 , Carlos Sánchez-López 3 , Luz del Carmen Gómez-Pavón 1 , Arnulfo Luis-Ramos 1 and Alejandro Israel Bautista-Castillo 2 1 Facultad de Ciencias de la Electrónica, Benemérita Universidad Autónoma de Puebla, Ciudad Universitaria, 18 Sur y Avenida San Claudio, San Manuel, Puebla 72000, Mexico; [email protected] (J.M.M.-P.); [email protected] (L.d.C.G.-P.); [email protected] (A.L.-R.) 2 Ingeniería en Electrónica y Telecomunicaciones, Universidad Politécnica de Puebla, Tercer Carril del Ejido Serrano S/N, San Mateo Cuanalá, Juan C. Bonilla, Puebla 72000, Mexico; [email protected] (C.M.-M.); [email protected] (A.I.B.-C.) 3 Facultad de Ciencias Básicas, Ingeniería y Tecnología, Universidad Autónoma de Tlaxcala, Apizaquito Km. 1.5, Apizaco, Tlaxcala 90300, Mexico; [email protected] * Correspondence: [email protected] Received: 27 January 2020; Accepted: 7 February 2020; Published: 21 February 2020 Abstract: A design strategy for the synthesis of high-selectivity/low-order analog filters in Complementary Metal-Oxide-Semiconductor (CMOS) technology for very high frequency (VHF) applications is presented. The methodology for the reconstitution of a given transfer function by means of Signal Flow Graphs (SFG) manipulation in canonical form is proposed leading to a fully differential gm-C biquad filter. As a practical example, the design of a notch filter intended to suppress interferers in the lower sideband (400 MHz) of the Medical Implant Communication Service (MICS), in single-poly, 6-metal layers; Mixed-Signal/RF 0.18 µm CMOS technology is realized.
    [Show full text]