EEWeb Pulse - Volume 14

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Issue 14 October 4, 2011

Alex Wyglinski Wireless Innovation Laboratory

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TA B L E O F C O N T E N T S TABLE OF CONTENTS

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Alex Wyglinski DIRECTOR, WIRELESS INNOVATION LABORATORY

Interview with Alex Wyglinski - Assistant Professor, Worcester Polytechnic Institute (WPI)

Changing the Way Wireless Technology Accesses Electromagnetic Spectrum

8

BY ALEX WYGLINSKI Wyglinski introduces his current project involving making wireless applications more spectrally efficient.

10 11

Featured Products The Long and Short of Vias

BY MICHAEL STEINBERGER WITH SISOFT Steinberger offers an alternate explanation for why vias are longer electrically than one might at first expect.

How Low Can You Go?

16

BY ROBERT GREEN WITH KEITHLEY Instructions for how to make accurate low-level current measurements.

RTZ - Return to Zero Comic

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INTERVIEW

Wireless Innovation Laboratory Alex Wyglinski - Assistant Professor at Worcester Polytechnic Institute (WPI)

almost everything I do, ranging from the implementation and testing of prototype vehicular communication networks to teaching senior-level courses in digital communication systems and SDR design.

Another hardware tool that is my favorite is my Agilent CSA N1996A spectrum analyzer. This device is frequently used by my research team and me to conduct experiments into wireless spectrum occupancy characterization, distributed spectrum sensing networks, and dynamic spectrum access algorithms.

How did you get into wireless communication systems engineering?

Ever since I was about five years old, I have always been mystified by society’s ability to communicate information wirelessly from one location to another without any cables. Although I am not sure what it is exactly that amazes me about wireless communications, whether it is the invisible nature of the data transmission or the freedom associated with untethered communications, I do know that once I got hooked on wireless communications starting with my very own pair of Fisher Price walkietalkies (I still have them in my office!), I was always thinking about how all this wireless stuff really

worked. Consequently, this guided my educational pursuits toward the sciences and technology, and ultimately to electrical engineering.

What are your favorite hardware tools that you use?

Both my research and educational activities are heavily focused on the design and implementation of highly adaptive, agile wireless communication systems and networks. Consequently, much of what I do at WPI involves the use of software-defined radio (SDR) platforms. Although there are numerous SDR platforms to choose from, I mostly use the Universal Software Radio Peripheral 2 (USRP2) by Ettus Research LLC for

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What are your favorite software tools that you use?

In 2008, one of my undergraduate summer research assistants wrote from scratch a Java program called SQUIRREL (Spectrum Query Utility Interface for Realtime Radio Electromagnetics) which controls the operations and parameters of the spectrum analyzer using SCPI commands and retrieves measurement data from the device and stores it on a laptop computer connected via Ethernet. As a result of SQUIRREL, it is possible to perform numerous spectrum measurements quickly, and efficiently store them away for postprocessing afterwards.

Other than SQUIRREL, I would say that both MATLAB and Simulink are my favorite software tools that I use fairly frequently, especially the Simulink USRP2 interface blocks available in the Communications Blockset of MATLAB 2010b. When

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FEATURED INTERVIEW

Alex Wyglinski

INTERVIEW

What is on your bookshelf?

Lots of communication systems engineering, digital communications, software-defined radio, and stochastic processes textbooks.

One item on my bookshelf that I am particularly fond and proud of is my first book, entitled Cognitive Radio Communications and Networks: Principles and Practice (Academic Press, December 2009). Co-edited with Maziar Nekovee (BT Research) and Tom Hou (Virginia Tech), and co-authored with dozens of international experts in cognitive radio communications and networks, this is the first textbook published on this subject. Although it took nearly two years to complete, it was definitely a worthwhile experience. In fact, I hope to include another textbook that I am in the process of writing on the subject of software-defined radio experimentation and design.

Why did you decide to pursue a career in academia?

I have always been very curious about how things work, ranging from computers to automobile engines. Ever since I was a young boy, I would try figuring out the details on how a particular device would operate while the

Ever since I was about five years old, I have always been mystified by society’s ability to communicate information wirelessly from one location to another without any cables. rest of the world would take it for granted. During my childhood, there were numerous occasions when I thought the best way to understand the operation of a device was to take it apart, much to the chagrin of my parents, especially when I dismantled the family stereo system.

Nevertheless, my curiosity about why various technologies worked, coupled with my passion for improving upon the current state-ofthe-art and my desire to disseminate knowledge and discoveries to the masses appeared to be a suitable

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match for a career in academia.

Although I graduated with my Ph.D. in 2005 and started my career in academia soon afterward, I still feel like a graduate student, albeit with a significant increase in the number of responsibilities. Nevertheless, pursuing a career in academia and work with the next generation of electrical and computer engineers has kept me young at heart and still full of passion and enthusiasm for this subject.

What has been your favorite project?

This is a hard question. There have been so many exciting, interesting, and fun projects that I have participated in throughout my career that I am not sure if there is a definite favorite. I think the most memorable projects that I have performed over the past several years all had to deal with wireless spectrum occupancy measurement and characterization. The first project took place in 2006 when I was at the University of Kansas.

Together with a couple of M.S. students, we proceeded to take TV spectrum measurements at various distances from a TV broadcasting antenna approximately 20 miles west of Topeka. It was fun being outside of the laboratory and trekking across the Konza Prairie collecting wireless spectrum measurements that were ultimately published in several peer-reviewed venues.

Another project involved taking wireless spectrum occupancy measurements across nearly 3 GHz (88 MHz to 3GHz) for four cities (Worcester, MA; Rochester, NY; Buffalo, NY; Pittsburgh, PA) at five locations per city. This NSFsponsored measurement campaign was conducted over a two week period during the summer 2008 with two NSF REU (Research

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FEATURED INTERVIEW

it comes to validating new concepts and ideas via accurate computer simulations, MATLAB and Simulink are always the first software tools that I utilize. Regarding the Simulink USRP2 blocks, there is a WPI story behind the development of these blocks. Via a collaborative relationship between the Mathworks and my research team at WPI, several projects have been pursued over the past three years that have been geared toward the integration of SDR prototyping hardware with MATLAB and Simulink.

Consequently, one of the first projects was to develop a prototype interface between Simulink and the USRP2 SDR platform. My former MS student, Michael Leferman, ultimately created these prototype transmitter and receiver blocks for his thesis, and served as a starting point for additional activities to integrate Simulink with the USRP2 hardware.

INTERVIEW

Yet another interesting project was conducted as part of some research collaborations with the Toyota InfoTechnology Center U.S.A. during summer 2009, when my Ph.D. student (Srikanth Pagadarai) and I took 10 frequency sweeps of TV spectrum at two-mile intervals along the entire Massachusetts portion of Interstate I-90. These sweeps would ultimately serve as a resource for evaluating designs of vehicular dynamic spectrum access (VDSA) networking systems. One of the highpoints of this measurement campaign was a side trip to Bunker Hill, which overlooks the city of Boston, at around midnight of June 12, 2009 when the DTV transition occurred.

Using the spectrum analyzer and SQUIRREL software tools, my Ph.D. student and I observed how history was being made in the U.S. with the deactivation of all analog TV channels and the reshuffling of several digital TV channels. It was quite a memorable experience!

What are you currently working on?

At the moment, there are at least three projects that I am currently working on with my research team.

The first project involves the design of a communication system that is capable of extracting wireless signals from a mixture of partially overlapping wireless transmissions using a well-known signal processing technique referred to as “blind source separation.”

Sponsored by the Office of Naval Research, the goal of this project is for a wireless communication system to extract a target wireless transmission that is being exposed to a substantial amount of interference, including jamming fields.

Finally, the third project involves establishing a link between the amount of available wireless spectrum within a geographical region and various socio-economic factors such as housing costs, education levels, or employment statistics. Although there have been several studies on assessing the amount of available spectrum within a specific region, there has not been any work yet on how this information is quantitatively linked to modern society. This project is being pursued by my Fulbright Fellow and Ph.D. student, Sean Rocke.

The second project is sponsored by the Toyota InfoTechnology Center U.S.A. and it involves the implementation of a vehicular networking test-bed that can perform multi-hop relay communications and dynamic spectrum access.

A team of undergraduate and graduate students have worked over the past nine months to construct a robust system using the USRP2 platforms and GNU Radio capable of operating in highway driving conditions. Just several weeks ago, nine of my students and I took five cars out for an experiment across a 60-mile distance in order to verify the functionality of the proposed implementation. Four of the cars were equipped with USRP2 platforms while the fifth car operated the spectrum analyzer that was monitoring the entire experiment. The results from this experiment were truly amazing, and my students and I are in the process of submitting a conference and journal paper based on this project.

Given the significant paradigm shift regarding how wireless communication systems are being designed, how they are operated, and how they are accessing wireless spectrum, it is very important to think creatively and look at other disciplines in order to gain a different viewpoint on how a communication system should work. This area is currently in a state of flux, and the rules are being rewritten as we speak—use this opportunity to be creative and shape the future of wireless communications and networking.

Using softwaredefined radio, what was considered impossible a decade ago can now be implemented in a relatively short period of time and with very little capital investment. Just remember that the future of wireless depends on our ideas now and making them a reality. ■

Do you have any advice for the next generation of wireless communication systems engineers?

Photos Taken By: Jennifer L. Brown - Cover Srikanth Pagadarai Supplementary Image

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FEATURED INTERVIEW

Experience for Undergraduates) students and one Ph.D. student packed into a rented minivan with a spectrum analyzer, antennas, and a bunch of other pieces of equipment. During this campaign, we took measurements from every possible location that was available to us, including the roofs of parking garages and parking spots across the downtown areas of the cities involved in this study. At the end of the study, several gigabytes of spectrum measurement data were collected, post-processed, and analyzed, yielding new and interesting insights on how spectrum occupancy changes as a function of spatial coordinates within an urban environment.

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PROJECT

Changing the Way Wireless Technology Accesses Electromagnetic Spectrum

By Alex Wyglinski

“Spectrum.” When one hears this word, images of a glass prism decomposing white light into a range of colors quickly comes to mind. However, electromagnetic (EM) spectrum impacts our lives well beyond the confines of a high school physics classroom.

With modern society undoubtedly entrenched in the Information Age heralded in by technological innovations introduced over a century ago by engineers such as Marconi, we have all become dependent on reliable access to information in order to function on a daily basis. Financial transactions, entertainment, educational activities, national defense, and public safety are just several of the numerous applications we as a modern society take for granted that are, in fact, heavily dependent on access to wireless spectrum.

Overall, modern society’s demand for access to wireless spectrum will only increase, and my research team here at WPI is at the forefront of ensuring that we all continue to enjoy the benefits of this highly important natural resource.

Despite the advantages of having unfettered and ubiquitous wireless access to support the daily activities of modern society, our dependence on wireless

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spectrum does come at a cost. Although not obvious at first, it turns out that wireless spectrum is actually considered to be a form of natural resource. Thus, given this definition, as well as examining the outcomes of several published spectrum measurement campaigns, the usable portions of wireless spectrum are a finite quantity for a specific geographical area. Consequently, coupling this finite resource with the constantly growing demand for wireless access, it is predicted that modern society will ultimately witness some form of wireless spectrum scarcity, thus limiting many of the modern conveniences we take for granted and affecting our quality of life.

To counteract the effects of spectrum scarcity, the wireless research community has been actively pursuing solutions that enable the applications that modern society depends on to be more spectrally efficient. Supported by federal regulators such as the U.S. Federal Communications Commission (FCC), researchers are devising new technologies and approaches that facilitate unlicensed (secondary) transceivers to “temporarily borrow” unused wireless spectrum from licensed (primary) entities. Referred to as dynamic spectrum access or DSA, this new wireless access paradigm possesses the potential to alleviate the spectrum scarcity issue being faced by modern society. However, there are numerous technical challenges that need to be resolved before DSA becomes a reality, in addition to the many policy and legal hurdles that require resolution. One of the core fundamental challenges in pursuing this DSA solution is to accurately assess which transceivers are occupying the wireless spectrum at any given time and location.

This can be achieved economically by understanding

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FEATURED PROJECT

Training an Old Dog to Learn New Tricks:

PROJECT

At the Wireless Innovation Laboratory (WI Lab) located at Worcester Polytechnic Institute (WPI) in Worcester, Massachusetts, my research team of graduate students, undergraduate students, and I are working on enhancing our understanding about wireless spectrum occupancy across several dimensions such as time, frequency, and space. My team of eight Ph.D. students, four M.S. students, and about half a dozen undergraduate students is actively looking at how wireless spectrum behaves such that novel approaches for performing DSA can be devised, as well as developing methods for more accurately assessing this behavior in real-time using several wireless spectrum sensor nodes distributed across a region. One project that was funded by the U.S. National Science Foundation (NSF) from 2007 to 2009 attempted to characterize wireless spectrum occupancy across 88 MHz to 3 GHz in four U.S. midsized cities. The results showed some correlation between the spectrum occupancy measurements and the population density, as well as the spatial correlation of the spectrum at two geographically separate locations.

The measurement data from this NSF-sponsored project can be found at the following website: http://spectrum.

ece.wpi.edu/. Building upon this project, my ongoing research collaborations with Toyota InfoTechnology Center U.S.A. from 2009 until the present have focused more on assessing the feasibility of performing DSA between vehicles, referred to as vehicular DSA or VDSA. Starting with a spectrum characterization of the television frequency channels along the Massachusetts portion of Interstate I-90, our research has focused on key issues for VDSA, such as the capacity of the unoccupied wireless spectrum to support secondary users and algorithms that can perform VDSA in vehicular operating environments.

Several emerging projects that my research team and I are working on include development of a distributed spectrum sensing network, which can be used by regulatory organizations in order to ensure that the spectrum is being accessed fairly by both primary and secondary users with minimal interference to nearby transceivers. We are also working on correlation of wireless spectrum occupancy measurements with socio-economic factors in order to determine the impact of wireless spectrum and our access to it on modern society.

Overall, modern society’s demand for access to wireless spectrum will only increase, and my research team here at WPI is at the forefront of ensuring that we all continue to enjoy the benefits of this highly important natural resource. ■

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the dynamics behind spectrum occupancy as well as developing networks of sensor devices that work in unison to provide the “big picture” regarding who is accessing the wireless spectrum.

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The

Long and the

Short of Vias

Mike Steinberger Lead Architect Serial Channel Products

P

ublished data indicates that vias are longer electrically than one might at first expect. The explanation that has been offered is that the effective dielectric constant for waves propagating perpendicular to the plane of the board is higher than that for waves propagating in the plane of the board. This article offers an alternate explanation, correlated with measured data, based on the electrical path around pads at the top and bottom of the via.

1.0 Surprising Data At DesignCon2009, both Eric Bogatin [1] and I [2] presented award-winning papers on the correlation of PC board via models with measured data. We both concluded that vias behave like TEM transmission lines whose

impedance can be calculated by assuming that there is a continuous shield at the edge of the antipad and running parallel to the via barrel (i.e., in the direction of the board thickness). My paper offered a motivation for this approximation by hypothesizing that radial TEM waves propagating from the edge of the antipad produced magnetic fields that approximately canceled the magnetic field from the via barrel. So far, this still appears to be a good approximation and explanation.

Bogatin’s paper went on to observe that the vias appeared to be longer electrically than one would have predicted based on the via length and the dielectric constant of the board. The explanation offered was that the effective dielectric constant

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for waves flowing in the direction of the board thickness (and therefore applying to vias) was higher than that for waves flowing in the plane of the board (and therefore applying to stripline traces). Further text can be found in [3].

I had not looked into electrical length at the time, but was openly skeptical. I’ve since been given access to more data, and that data forced me from denial to perplexity.

Of course, one must first account for the effects of the ground impedance in either single-ended or common mode [4]. However, even the differential mode electrical length was a little longer than expected.

Subsequent analysis has brought me to a higher level of understanding, albeit based on a very different explanation.

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TECHNICAL ARTICLE 0.4

Insertion Phase (radius)

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This year I received some very high quality data measured padto-pad for differential traces on an unpopulated backplane. Each trace measured consisted of a differential via, a differential backplane trace, and another differential via.

Thirteen paths were measured on six different layers of the backplane.

The analysis approach was to convert the measured S parameters to time domain reflectometry (TDR)

Model

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Figure 1: Modeled and measured phase from the Simple Via Experiment.

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2. Within experimental error, and at least for very high loss PC board material, it appears that the dielectric constant is isotropic.

In short, the vias in this study exhibited the insertion phase expected of them.

Sample 2

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Note that Figure 1 shows the modeled insertion phase with and without the effects of the ground impedance. Figure 1 suggests two conclusions: 1. Ground impedance can have a significant effect on insertion phase.

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Figure 2: Bare backplane via electrical length vs. physical length.

plots and measure the electrical length of the via at the beginning of the trace in both single-ended and differential modes. Since the measurement was made from pad to pad, the launching via was the very first element in the electrical path, so relatively precise measurements could be made.

The measurement bandwidth was 20GHz, producing an effective TDR rise time of 17pS. The measured electrical length

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versus physical length is shown in Figure 2. In this Figure, the single-ended measurements are shown using yellow symbols and the differential measurements are shown using red symbols. A line is extended through the measured differential values.

The line in Figure 2 was drawn through the differential mode data points because they show less spread than the single-ended data points, and also because differential

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TECHNICAL ARTICLE

2.0 My Data The first data I looked at was from our DesignCon2009 paper. At the time we wrote the paper, we had concentrated on insertion loss.

I went back and calculated the insertion phase for two, nearly identical single-ended samples, using the assumption that the dielectric constant is isotropic. Note also that this data was measured on low cost, very high loss material purchased at a well-known consumer electronics store. The results are shown in Figure 1.

TECHNICAL ARTICLE

The slope of the line in Figure 2 corresponds to a dielectric constant of 3.61, compared to a dielectric constant of 3.41 for the traces on this particular PC board. This difference is within experimental error.

The most interesting aspect of Figure 2, however, is the fact that zero physical length does not appear to correspond to zero electrical length.

Rather, there appears to be a length offset of about 0.05”. While this is clearly evidence for an electrical length greater than that predicted, it is not consistent with the hypothesis that there is an increased dielectric constant for waves propagating perpendicular to the plane of the board.

3.0 “If you were the signal, where would you go?”

The via and trace depicted in Figure

3 consists of a top pad (typically at the top surface of the board), via barrel, bottom pad (typically at an internal routing layer), and exit trace (routing from the edge of the pad to the edge of the antipad).

It is instructive to consider where the current would actually flow on such a structure. The currents generate magnetic fields which in turn force the currents to the outer surfaces of the structure. This is the explanation of the well-known skin effect, for example. Thus, current injected at the top surface of the top pad will not penetrate straight through the pad to get to the via barrel. Rather, it must spread out across the top surface of the top pad, flow over the edge of the top pad, and converge across the underside of the top pad to get to the via barrel.

Similarly, at the bottom end of the via barrel, the current must flow across the upper surface of the bottom pad to get to the exit trace. Some of the current will also flow to the edge of the bottom pad, over the edge, and across the underside of the bottom pad to get to the underside of the exit trace.

Note the following measurements used to produce Figure 2: • The distance across the upper surface of the top pad • The distance across underside of the top pad

the

• The distance across the upper surface of the bottom pad

Figure 3: Via current flow

• The length of the exit trace from the edge of the bottom pad to the edge of the antipad

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When adding up each of these distances and lengths, the total distance is 0.05”—the missing physical length shown as an offset in Figure 2. This is partially a coincidence in that there is a lumped capacitance at the edges of the top and bottom pads, and the exit trace will tend to have a higher impedance than the nominal trace impedance, whereas the rest of the via will have a much lower impedance. Nonetheless, data and physical reasoning support the hypothesis that the current paths across the top and bottom pads of a via measureably increase its electrical length.

Figure 4 and Figure 5 are representative examples of the model correlation obtained using post-route extraction with closedform equations and a via model that explicitly models the electrical length of the pad. The measured results are shown in blue and the model results are shown in red.

In Figure 4, the time differences between the model and the measurements are much less than the 17pS rise time of the data, and the impedance differences are less than the impedance difference between the two ends of the trace (e.g., as measured at 400pS).

Thus, the TDR data is well within experimental error. Similarly in Figure 5, the model matches the frequency domain ripples of the loss characteristic precisely and even at 20GHz the difference between model and measurement is less than 3dB. The results shown are for a bare backplane; however, the correlation is almost as good when connectors

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TECHNICAL ARTICLE

mode is not affected by the ground return impedance. Note also that the single-ended delays tend to be higher than the differential delays for the same vias, providing some evidence for an increase in delays due to ground return impedance, similar to the effect shown in Figure 1.

TECHNICAL ARTICLE BLUE: Measured Data

RED: Model

[3] Eric Bogatin, Bert Simonovich, and Yazi Cao, “Practical Design of Diifferential Vias”, Printed Circuit Design and Fab magazine, July 7, 2010. http://pcdandf.com/cms/ component/content/ar ticle/171current-issue/7302-eric-bogatinbert-simonovich-and-yazi-cao

100.0

DB

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[4] Mike Steinberger, “Via Return Currents and the Path of Least Resistance”, EEWeb magazine, June 6, 2011. http://www.eeweb. com/blog/michael_steinberger/ via-return-currents-and-the-path-ofleast-resistance

600.0

Time ( s)

Figure 4: Typical differential TDR correlation result.

Differential Mode Transfer Function BLUE: Measured Data

RED: Model

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About the Author

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Figure 5: Typical differential insertion loss correlation result.

and line cards are added.

4.0 Conclusions The electrical length of vias is longer than that predicted by via length and dielectric constant. This additional electrical length seems to be constant with respect to via length.

The length of the current path around the top and bottom pads associated with a via is comparable

to the additional electrical length for the via.

5.0 References [1] E. Bogatin, L. Simonovich, C. Warwick and S. Gupta, “Practical Analysis of Back- plane Vias for 5 Gbps and Above”, paper 7-TA2, DesignCon2009, February 3, 2009.

Michael Steinberger, PhD, has over 30 years experience in the design and analysis of very highspeed electronic circuits. Dr. Steinberger began his career at Hughes Aircraft, designing microwave circuits. He then moved to Bell Labs, where he designed microwave systems that helped AT&T move from analog to digital long-distance transmission. He was instrumental in the development of high-speed digital backplanes used throughout Lucent’s transmission product line. Prior to joining SiSoft, Dr. Steinberger led a group of over 20 design engineers at Cray, Inc. responsible for SerDes design, high-speed channel analysis, PCB design, and custom RAM design. ■

[2] Chong Ding, Divya Gopinath, Steve Scearce, Mike Steinberger, Doug White, “A Sim- ple Via

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TECHNICAL ARTICLE

Experiment”, paper 5-TP2, DesignCon2009, February 3, 2009.

Interconnect Network Differential TDR Response

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TECHNICAL ARTICLE

How Low Can You Go? The increased use of portable and remote electronic devices, such as smartphones and tablet computers, in combination with the desire to conserve battery power, is driving semiconductor manufacturers to develop devices that operate at extremely low current levels. To characterize these devices, test engineers must be able to make very low-level current measurements.

This is often a real challenge because the level of the current is often at microamp current levels. When making low-level measurements, minimizing noise is crucial.

A number of instruments are available for measuring low currents: • Digital multimeters. The most sensitive DMMs available can measure currents as low as about 10pA. • Ammeters. Ammeters optimized for measuring very low currents are known as picoammeters and are available in various configurations, including highspeed models and logarithmic units capable of

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displaying a wide current range.

• Source-Measure Units (SMUs). SMUs combine precision current and voltage sources with sensitive voltmeters and ammeters. An SMU can simultaneously provide a source of current and measure voltage or provide a source of voltage and measure current. These instruments can also be high precision loads for testing batteries and other power sources.

Minimizing Noise No matter which instrument you choose, noise, both internal and external to the instrument, will limit its sensitivity. The device under test (DUT) plays a part in the level of current that can be detected with a given instrument because the DUT’s source resistance (RS) establishes the level of Johnson current noise (IJ), the lowlevel noise caused by temperature effects on electrons in a conductor. Johnson noise, which can be expressed in terms of either current or voltage, is essentially the voltage noise of a device divided by the device resistance.

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TECHNICAL ARTICLE 10-6

1nA

10-9

1pA

10-12

John

son

1{A

Curr

1aA 103

1kΩ

106

1MΩ

109

1GΩ

10-15

ent N

oise

1012 1TΩ

1015 1PΩ

10-18 1018 1EΩ

Source Resistance Figure 1: Johnson current noise is dependent upon many factors, including the source resistance of the DUT

Both temperature and noise bandwidth affect the Johnson current noise. A reduction in either parameter will also reduce the Johnson current noise. Cryogenic cooling, for example, is often used to reduce noise in amplifiers and other circuits but adds cost and complexity. Reducing the noise bandwidth by filtering will also lower the Johnson current noise, but this will slow the measurement speed.

Noise current can come from many sources, including the coaxial cable used to connect test instruments to each other or to the DUT. When typical test cables are flexed, they can generate as much as tens of nanoamps of current when the outer shield of a coaxial test cable rubs against the cable’s insulation. When this occurs, electrons are stripped from the insulation and added to the current total. In some applications, such as nanotechnology and semiconductor research, the current generated by this triboelectric effect may exceed the level of current to be measured from the DUT. You can reduce the noise due to the triboelectric effect by using low-noise cable, with an inner insulator of polyethylene coated with graphite underneath the outer shield. Other ways to reduce triboelectric effect noise is to keep test cables as short as possible and to isolate them from vibration to minimize movement.

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Dirty or contaminated insulators may also contribute noise currents. On an insulator, the contamination forms a low-current battery at a sensitive current node within the insulator. These noise currents can be several nanoamps. To minimize measurement errors from insulator contamination, an operator should wear gloves when handling insulators or simply avoid touching them.

The use of solder should be minimized, and solder areas should be cleaned with an appropriate solvent, such as isopropyl alcohol.

Finally, stray magnetic fields may generate noise currents. Properly shielding instruments and test fixtures will help prevent false readings.

As you can see, making low-level current measurements can be quite complex. But, by paying close attention to test system and fixture design, you can minimize current noise and make accurate measurements.

For more information 1. Low Level Measurements Handbook, sixth edition, Keithley Instruments, Cleveland, OH, http://www. keithley.com/knowledgecenter/ About the Author Robert Green is a Senior Market Development Manager at Keithley Instruments focusing on low level measurement applications. During his 20-year career at Keithley, Mr. Green has been involved in the definition and introduction of a wide range of products including picoammeters, electrometers, digital multimeters, and temperature measurement products. He received a B.S. in Electrical Engineering from Cornell University and an M. S. in Electrical Engineering from Washington University, St. Louis, Missouri. ■

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TECHNICAL ARTICLE

1µA

Putting a mechanical stress on a DUT can also cause a low-current measurement error. This piezoelectric effect varies by material. Polytetrafluoroethylene (PTFE) dielectrics, for example, can produce relatively large currents for a given amount of stress and vibration, while ceramic materials produce lower current levels. To minimize the amount of current generated by this effect, minimize mechanical stress on insulators and construct any low-current test system using insulating materials with minimal piezoelectric properties.

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Low-Noise 24-bit Delta Sigma ADC ISL26132, ISL26134

Features

The ISL26132 and ISL26134 are complete analog front ends for high resolution measurement applications. These 24-bit Delta-Sigma Analog-to-Digital Converters include a very low-noise amplifier and are available as either two or four differential multiplexer inputs. The devices offer the same pinout as the ADS1232 and ADS1234 devices and are functionally compatible with these devices. The ISL26132 and ISL26134 offer improved noise performance at 10Sps and 80Sps conversion rates.

• Up to 21.6 Noise-free bits.

The on-chip low-noise programmable-gain amplifier provides gains of 1x/2x/64x/128x. The 128x gain setting provides an input range of ±9.766mVFS when using a 2.5V reference. The high input impedance allows direct connection of sensors such as load cell bridges to ensure the specified measurement accuracy without additional circuitry. The inputs accept signals 100mV outside the supply rails when the device is set for unity gain.

• On-chip temperature sensor (ISL26132)

• Low Noise Amplifier with Gains of 1x/2x/64x/128x • RMS noise: 10.2nV @ 10Sps (PGA = 128x) • Linearity Error: 0.0002% FS • Simultaneous rejection of 50Hz and 60Hz (@ 10Sps) • Two (ISL26132) or four (ISL26134) channel differential input multiplexer • Automatic clock source detection • Simple interface to read conversions • +5V Analog, +5 to +2.7V Digital Supplies • Pb-Free (RoHS Compliant) • TSSOP packages: ISL26132, 24 pin; ISL26134, 28 pin

Applications

The Delta-Sigma ADC features a third order modulator providing up to 21.6-bit noise-free performance.

• Weigh Scales

The device can be operated from an external clock source, crystal (4.9152MHz typical), or the on-chip oscillator.

• Temperature Monitors and Controls

The two channel ISL26132 is available in a 24 Ld TSSOP package and the four channel ISL26134 is available in a 28 Ld TSSOP package. Both are specified for operation over the automotive temperature range (-40°C to +105°C).

• Pressure Sensors

• Industrial Process Control

CAP

AVDD

DVDD

INTERNAL CLOCK

EXTERNAL OSCILLATOR

XTALIN/CLOCK XTALOUT

AIN1+ AIN1AIN2+ AIN2AIN3+ AIN3-

SDO/RDY INPUT MULTIPLEXER

PGA 1x/2x/64x/ 128x

ADC SCLK

ISL26134 Only AIN4+ AIN4PWDN

SPEED

A0

A1/TEMP AGND

GAIN0 GAIN1

CAP

DGND

VREF+

VREF-

DGND

DGND

NOTE for A1/TEMP pin: Functions as A1 on ISL26134; Functions as TEMP on ISL26132

FIGURE 1. BLOCK DIAGRAM

September 9, 2011 FN6954.1

Intersil (and design) is a registered trademark of Intersil Americas Inc. Copyright Intersil Americas Inc. 2011 All Rights Reserved. All other trademarks mentioned are the property of their respective owners.

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