Signal conditioning provides a distinct advantage because it enhances both performance and measurement accuracy.

For many real-world applications, you must measure environmental or structural parameters, such as temperature or vibration, with sensors. These sensors, in turn, require signal conditioning before a data acquisition device can effectively and accurately measure the signal. Signal conditioning provides a distinct advantage over data acquisition devices alone because it enhances both the performance and measurement accuracy of data acquisition systems.

Data acquisition systems
With the speed and accuracy of modern data acquisition devices, it is easy to overlook the need for signal conditioning. While plug-in DAQ devices specifically and accurately measure voltage signals, voltage is only one of many I/O types required by modern measurement and automation applications.

Many of today’s data acquisition systems must also measure signals from sensors that detect physical, chemical, or mechanical phenomena. While several of these sensors, such as RTDs and strain gauges, must have signal conditioning to return any measurement, they all require conditioning to return accurate measurements.

Fig 1. New signal conditioning systems can perform multiple sensor measurements in a single system.

While data acquisition devices have become progressively more intricate, the basic principles of data acquisition remain the same — you must connect to the signal, apply the necessary signal conditioning, digitize the signal, and display the data (see Fig. 1). With this in mind, the three vital components of all data acquisition systems are as follows:
• Signal conditioning (to condition the signal/sensor).
• Data acquisition device (to digitize the conditioned signal).
• Software (to analyze, record, and display the acquired signal data).

The component most often forgotten, yet fundamentally important, is signal conditioning. A large portion of the world’s measurable signals must be detected with sensors, most of which require some sort of signal conditioning for the data acquisition device to accurately read them. Thus, a data acquisition system must not only incorporate the digitizer and application software, but also tightly integrated signal-conditioning hardware.

Improving accuracy
Data acquisition devices are used in a variety of applications. In laboratories, in field services, and on manufacturing plant floors, these devices act as general-purpose measurement tools well suited for measuring voltage signals.

However, for many real-world applications, you must measure environmental or structural parameters, such as temperature or vibration, with sensors. These sensors, in turn, require signal conditioning before a data acquisition device can effectively and accurately measure the signal. Signal conditioning provides a distinct advantage over data acquisition devices alone because it enhances both the performance and measurement quality of data acquisition systems.

To illustrate the necessity of signal conditioning, consider a thermocouple. To accurately measure thermocouple signals, you must provide amplification, filtering, and cold-junction compensation.

Amplification is required because of the small magnitude of the signal, and you must apply it as close to the thermocouple as possible to increase your signal-to-noise ratio. While this amplification help reduces the noise effect on your signal, you must also provide filtering to eliminate environmental noise from power lines and other electric devices.

Cold-junction compensation is also necessary to offset any temperature difference that exists between the measurement junction of the thermocouple and the junction with the data acquisition device. The net effect of this signal conditioning is dramatically improved accuracy.

Fig. 2. SCB-68 screw-terminal connector block.

Fig. 3. SCXI-1112 thermocouple signal conditioner.

The graph compares thermocouple measurements taken at 25°C using a National Instruments SCXI-1112 thermocouple signal-conditioning module and an SCB-68, a screw terminal connector block with a temperature sensor for cold-junction compensation (see Figs. 2 and 3). The SCXI-1112 module achieved an accuracy of 0.3°C, compared to 5.0°C accuracy with the SCB-68 (see Fig. 4). Thus, the SCXI-1112 signal-conditioning module provides a thermocouple measurement with accuracy more than 10 times greater than that of the terminal block because of preamplification, low-pass filtering, and a more accurate temperature sensor.

Fig. 4. This accuracy comparison shows that the SCXI-1112 signal-conditioning module delivered ±0.3°C accuracy compared to ±5.0°C with the SCB-68 terminal block using a calibrated input.

There are several critical signal conditioning technologies that enhance the accuracy and performance of the data acquisition system:

Amplification. Amplifiers improve the accuracy and sensitivity of your small signal measurements by boosting the amplitude of the input signal to better match the input voltage range of the digitizer, thereby increasing the resolution and sensitivity of the measurement. While many data acquisition devices include onboard amplifiers for this reason, many sensors, such as thermocouples,
require more amplification than a data acquisition device alone can provide. Using signal conditioning to amplify the signal near the source also reduces the environmental noise effect on your measurement.

Attenuation. Attenuation diminishes your input signal’s amplitude to fall within the digitizer’s input range so you can measure high-voltage signals with your data acquisition system.

Isolation. Signal-conditioning devices with isolation pass input signals to the measurement device by using transformer, optical, or capacitive coupling techniques rather than a physical connection. Isolation prevents ground loops. With isolation, you can measure signals with high common-mode voltages while protecting the expensive measurement equipment in your data acquisition system from any high-voltage surges that may occur.

Filtering. Filtering improves your measurement accuracy by removing unwanted frequency components from your signal. In addition to eliminating noise from your measurement, filtering prevents signal aliasing (a phenomenon that occurs when frequencies higher than half of the sampling rate appear in your measured signal, corrupting your measurement).

Excitation. Many sensors, such as RTDs, strain gages, and accelerometers, require some form of power to return a measurement. Excitation provides this power, in the form of either voltage or current, so you can use these types of sensors in your data acquisition system.

Calibration. Calibration improves your measurement accuracy by adjusting your data acquisition system to compensate for any imbalances in your sensor or measurement hardware. For example, strain gage measurements require both null (or zero) and shunt (or gain) calibrations to ensure accurate linearization.

Cold-junction compensation. Thermocouples measure temperature as the difference in voltage between two dissimilar metals. Based on this concept, another voltage is generated at the connection between the thermocouple and connector (or terminal) block of your data acquisition device.

Cold-junction compensation improves your temperature measurement accuracy by providing the temperature at this connection, which you can then subtract from the reading.

Simultaneous sampling. When you must measure two or more signals at the same instant in time, you need simultaneous sampling. Using signal conditioning with track-and-hold circuitry can be a much more cost-effective simultaneous sampling solution than purchasing a digitizer for each channel. Typical applications that might require simultaneous sampling include vibration measurements and phase-difference measurements (see Table 1).

DAQ system considerations
The number of available data acquisition system devices and options can make the process of choosing the proper components very complex. But this process is crucial because the type of components you use can have a dramatic effect on the overall performance and accuracy of your system. Couple this with the fact that your development and time to first measurement also can be drastically impacted, and it quickly becomes evident that component choice is one of your most important decisions in selecting the right data acquisition system.

There are nine essential considerations for your data acquisition system that can help you take full advantage of the latest advances in computer-based data acquisition.

Breadth of signal types. Selecting signal conditioning hardware that accepts a large breadth of signal types is critical to protecting your data acquisition system investment. In addition, the ability to incorporate all of these measurements into a single data acquisition system can dramatically reduce your development time because you can focus on implementing your tests rather than learning and configuring multiple measurement systems. To illustrate, consider an application where you must validate the design of an automobile engine. To accurately characterize the engine, you must measure a variety of signal types — including temperature, vibration, frequency (rpm), and torque — each with unique conditioning requirements. Traditionally, this meant that you needed an individual stand-alone instrument or custom data acquisition device for each type of measurement, which required you to configure multiple devices. With modern, high-performance signal-conditioning hardware, you can easily incorporate all of these measurements into a single, rugged chassis and configure them from a single software interface, such as NI-DAQ. This capability reduces your current application’s development time and cost while still protecting your data acquisition system investment and providing the flexibility to address future applications.

Connectivity. With the diverse range of sensor connectors available, your signal-conditioning hardware must not only offer a variety of connectivity options but also, more importantly, the specific options you need. Whether you are using a strain gage with a D-Sub connector or an accelerometer with a BNC interface, your signal-conditioning platform should offer easy connection to all of your sensors to simplify your system setup. Some signal-conditioning hardware offers direct connectivity options on a per-channel basis so you can match each channel to the required connector. With sensor-specific connectors, you can easily remove and replace individual sensors while your data acquisition system is still running, making it easier to troubleshoot your system and minimizing system downtime. On the other hand, the most flexible type of connector is the screw terminal. Consider a data acquisition system with screw terminals for voltage and current measurements or if your sensor connection type is likely to change often. When you can connect your data acquisition system to any sensor, you greatly enhance your measurement capabilities.

Expandability. As your test evolves and your measurement requirements change, you must have a data acquisition system that provides the flexibility to expand and change with your application. Expanding your data acquisition system should not require a complete overhaul of your signal-conditioning platform. Using modular signal-conditioning hardware, you can very quickly increase the number and variety of signals in your system by simply plugging in another module. This feature protects your data acquisition system investment because you can expand your channel count in a matter of minutes, dramatically reducing the time before your modified system is up and running. This flexibility, in turn, reduces the total cost of ownership for your data acquisition system.

Integration. To realize the full productivity potential and value of your data acquisition system, all of its components must integrate seamlessly. Specifically, your signal-conditioning hardware should be capable of incorporating mixed-signal types in a single system, while still maintaining quick and easy connection to your data acquisition device. With this capability, you can dramatically reduce your setup time. Furthermore, by selecting signal-conditioning hardware that tightly integrates with your data acquisition device, you can easily upgrade the speed and resolution of your entire data acquisition system as your application requirements evolve by simply upgrading the data acquisition device. Thus, tightly integrated signal-conditioning hardware can reduce both current and future system development costs.

Packaging. Your signal-conditioning hardware packaging is most often dictated by the size and environmental constraints of your application. Because space is at a premium on most laboratory and test floors, it is important to choose a data acquisition system that packs more channels into less space. Signal conditioning with high-channel density minimizes the space requirement of your data acquisition system while reducing per-channel cost. In portable applications, your signal-conditioning hardware must be compact and lightweight, while still offering a high level of performance and functionality. Alternatively, applications running in harsh, industrial environments require signal conditioning with rugged mechanical packaging. To operate effectively in such extreme environments, hardware must be capable of enduring a wide operating temperature range in addition to severe shock and vibration.

Software. A large portion of the total cost of a test and measurement system is application development. To keep these costs to a minimum, you must use software tools that maximize productivity. In particular, driver software should provide a single interface for configuring and testing your entire data acquisition system, while also tightly integrating with your application development environment (ADE). Driver software should also provide the ability to scale and calibrate your sensor measurements. These capabilities dramatically reduce your overall development time and cost because you can quickly incorporate new sensor measurements into your data acquisition application.

Isolation. Isolation can dramatically increase the overall value of your data acquisition system by improving overall safety, accuracy, and performance. By creating an insulation barrier, isolation permits the ground reference of the input and output of a measurement device to be at different voltage levels, protecting both the operator and equipment from any transient voltage spikes. Isolation also improves system accuracy by physically preventing ground-loop currents, a common source of measurement noise and inaccuracy; ground loops result when a data acquisition system and its input signal have separate grounds at different potentials. Lastly, isolation improves the performance of your data acquisition system by increasing its common-mode rejection ratio (CMRR), or ability to reject common-mode voltage. Common-mode voltage, another frequent source of error, is voltage that is present on both the positive and negative input of your measurement device, but it is not part of the signal you wish to measure. While isolated devices are often more expensive, their additional cost is easily justified when you consider the amount of troubleshooting time isolation saves you by eliminating hard-to-find sources of error, such as ground loops and
common-mode voltage.

Calibration. One of the most critical technologies that a signal-conditioning system should incorporate is the ability to be easily and accurately calibrated. Most measurement devices are calibrated at the factory, but the accuracy immediately starts to drift with time and temperature changes. To make the most accurate measurements possible, you must periodically calibrate your entire data acquisition system. If your system has precision onboard voltage references, you can adjust your measurement system to compensate for temperature changes. In addition, you must have access to external calibration services to keep your system performing up to the manufacturer’s specifications year after year. It is very important to understand the calibration capabilities and requirements for any signal-conditioning system under consideration because this is the only way to ensure that your investment contains the technology you need to make accurate and reliable measurements.

Switching. In today’s demanding test environments, the ability to route signals easily throughout your measurement system is a technology that can lead to huge improvements in test times. As an example, consider a case where you must subject a unit under test (UUT) to four separate measurements in the testing process. Without the proper technology, you must reconnect the UUT to each different measurement device for each test. With state-of-the art switching technology, not only can you route the UUT leads automatically to each measurement device in turn, but also you can test several UUTs at the same time. You thus achieve more efficient use of your test equipment, faster test times, and less user intervention. Your selection of a signal-conditioning system that offers this technology can have a huge impact on the overall performance and cost of your system.

Bandwidth. Bandwidth is an often overlooked but extremely important technology to consider when selecting a signal-conditioning system. Modern signal-conditioning hardware should have the bandwidth to handle data throughput from a high-channel-count system and to accommodate any future growth in channel count. System bandwidth is typically expressed in samples/second (Hz), and often reaches several hundred kilohertz for large systems even at modest acquisition rates.

Overall, signal conditioning defines the measurement capabilities and is a critical component of any complete data acquisition system. Furthermore, signal conditioning is required for accurate sensor measurements. To protect your data acquisition system investment, you must invest in modular, easily expandable signal-conditioning hardware that accepts a wide variety of signal types and offers a broad range of connectivity options, while still meeting your size and environmental constraints and tightly integrating with your development software and data acquisition device.

The types of hardware listed in Table 2 are examples of National Instruments offerings. They serves as an example of the types of choices available to users when selecting signal-conditioning hardware capable of interfacing a signal or sensor to a data acquisition system.

Front-end signal conditioning (SCXI)
SCXI is a signal-conditioning and data acquisition system for PC-based instrumentation applications (see Fig. 5). It consists of a shielded chassis that houses a combination of signal-conditioning input and output modules that perform a variety of signal-conditioning functions. You can connect many different types of transducers, including thermocouples, directly to the modules. The system is a high-performance USB plug-and-play data acquisition system, and it can also operate as a front-end signal-conditioning system for PCI, PXI, or PCMCIA data acquisition devices.

Fig. 5. Front end signal conditioning systems convert sensor measurements to a more standard 10V signal to be acquired by another data acquisition device.

Integrated DAQ and signal conditioning (SC series)
SC Series data acquisition (DAQ) devices (see Fig. 6) expand the measurement capability of PXI by integrating measurement-specific signal conditioning onto a 16-bit PXI data acquisition device. With this tight integration of signal-conditioning and data-acquisition functionality, the SC Series delivers high-performance sensor-specific measurements at a lower cost per channel than leading solutions, such as SCXI DAQ systems, for low- to medium-channel counts.

Fig. 6. Combined Signal conditioning and DAQ devices are available for internal form factors such as PCI and PXI.

Distributed DAQ with signal conditioning
CompactDAQ (see Fig. 7) and CompactRIO are modular embedded control and distributed I/O systems for measurement, control, and data logging. They are intended for applications that demand industrial-grade hardware with easy installation and configuration. Both systems feature built-in signal conditioning for direct connectivity to sensors and actuators. Modules are available for connecting to thermocouples, RTDs, strain gauges, 4 to 20-mA signals, high-voltage sources, and many other signals.

Fig. 7. New signal conditioning instrumentation can be customized or expanded by adding different modules.

They offer embedded control by running LabVIEW Real-Time on a dedicated embedded processor, and can connect to a PC through a variety of industrial buses (Ethernet, serial, CAN, and Foundation Fieldbus) or even wirelessly (see Fig. 8). They can operate in harsh environments with electromagnetic noise, wide temperature ranges, and high shock and vibration.

Fig 8. Decreasing size and power requirements for signal conditioning help enable wireless test devices.

National Instruments
www.ni.com

An encoder’s coefficient of expansion and its tolerances will play a more significant role in future ISO standards for classifying coordinate measuring machines.

A measuring room offers optimum conditions for precise measurements. But it has several disadvantages including high costs for the room, the machine and temperature stabilization, as well as interruption in the flow of production.

Customers continue to push to install more control over the manufacturing process. Part of this push involves placing the measuring machine spatially closer to actual production, a modification known as production-integrated measurement. Through such a modification, measurement results can go “online” into the control of production and thereby affect the precision of the manufacturing process.

However, the harsh nature of a typical manufacturing environment places new requirements on measuring machines. These requirements either did not exist or were less critical in the sheltered surroundings of a measuring room.

Measuring machines on the shop floor are exposed to changing temperatures and more difficult ambient conditions. Shock, vibrations, and contamination occur often. Manufacturers of measuring machines are responding to these requirements with various designs and approaches. However, all are in agreement on one point: Deviations from the 20° C reference temperature specified in DIN 102 change the length and angle on both the work piece and the measuring machine, and these changes must be mathematically compensated.

KNOWN BEHAVIOR
The defined, reproducible thermal behavior of the encoder is indispensible for accounting for such deviations. The encoder’s coefficient of expansion and its tolerances will play a more significant role in future ISO standards for classifying coordinate measuring machines (see ISO TC 213-WG 10).

Thermal expansion = change of length – an unknown quantity

The coefficient of expansion, or deviations from it, influence the use of encoders on measuring machines. Encoders usually feature measuring standards of steel, glass, or glass ceramic.

The relevant literature provides data for the coefficients of expansion; however the data given differ significantly from source to source. Thus, their utility as a basis for length compensation is limited, as becomes visible in the data for steel, for example. A temperature change of even a few degrees can result in deviations of several micrometers in compensation values calculated from an inaccurate coefficient of expansion.

The scanning heads for the LIDA 400 are a standard size, so they meet all requirements for reading the scales of glass and glass ceramic. Also, the identical cross section of the scales allows the graduation carriers to be exchanged.

POSSIBLE METHODS OF ASCERTAINING THE COEFFICIENT OF EXPANSION α
A coefficient of expansion can be measured exactly by a dilatometer, which is a device for measuring thermal expansion. With a well-designed dilatometer it is possible to attain exact data on a material’s coefficient of expansion by measuring a test object and use it to manufacture encoders.  An example is the “alpha measuring station” for measuring the thermal length expansion of bar-shaped bodies. Such a measuring station has been set up at the Physikalisch-Technische Bundesanstalt, Germany’s national metrological institute in Brunswick.

This exactly measured value can then be applied to calculate length compensation. In most cases, companies manage as best they can with data from the literature or the material manufacturer. This makes uncertainty in the result inevitable.

TEMPERATURE AND ACCURACY COMPENSATION
Special care must be taken in setting up a shop-floor measuring machine. Years of experience by the manufacturer result in high reliability and ensure high accuracy in spite of harsh environmental conditions. No compromises in accuracy are made compared with machines in measuring rooms.

Thermal effects must be dealt with through the appropriate know-how, the selection of suitable materials, and providing for thermal requirements. Because temperature increases expand materials to different degrees and these materials take on the surrounding temperature at different speeds, complex calculations are conducted to compensate the effects of temperature and accuracy. A known basis for mathematical compensation is very important—the linear encoder.

Thermally stable encoders are an indispensible prerequisite for basing calculations on accurate measurement data and thereby achieving accurate compensation. The selection of encoder material for shop floor measuring machine is therefore particularly important. While glass or steel scales permit only an approximate value for calculation, the expansion coefficient of 0+/- 0.1 x 10-6K-1 ZERODUR® for glass ceramic scales remains accurate over a large temperature range, and the scales have proven to be durable. The material is used the world over on telescopes, for example, because they place very high requirements on resistance to temperature changes and on distortion-free imaging.

THERMALLY STABLE ENCODERS
The right encoder enhances machine characteristics and contributes significantly to the reliability of the measuring machine. The area of production-integrated measurement is characterized by the following requirements and characteristics:

• Encoders with defined coefficients of expansion
• High accuracy for deviation between compensation points
• Minimal contamination for disturbance-free measurement
• High reliability over a long time period
• Cost-efficient encoders

One type of encoder that meets these requirements is the LIDA 400 exposed incremental encoder. Features include high accuracy and liberal mounting tolerances, high traversing speed, and the small height of the scanning head.

These attributes make it well suited for use on production equipment in automation engineering and the electronics industry as well as for applications on linear drives and in many areas of metrology.

The introduction of new graduation carriers of glass and glass ceramics, such as ZERODUR® and ROBAX®, have expanded the range of applications covered by encoders. They therefore suit applications in shop-floor measuring machines. They are easily installed by the PRECIMET® adhesive film on the back.

The scanning heads for the LIDA 400 are a standard size, so they meet all requirements for reading the scales of glass and glass ceramic (ZERODUR®, ROBAX®). No special scanning heads are needed. Also, the identical cross section of the scales allows the graduation carriers to be exchanged. From the logistical point of view this is a great advantage because the standard LIDA 48 (1 VPP) and LIDA 47 (TTL) scanning heads can be combined with glass ceramic and glass scales as well as with steel scale tapes. The identical carrier cross section of glass ceramic and glass scales make it possible to upgrade existing measuring machines. All designs have the same scanning surface of 14.5 mm², which ensures high tolerance to contamination and generates very clean scanning signals, which can be highly interpolated.

The encoders of the LIDA 400 series have a grating period of 20 µm. They are available in the widely used 1VPP and TTL interfaces and for measuring lengths of up to 30 m (steel) or 3 m (glass and glass ceramic). Traversing velocities up to 480 m/min are possible. The encoders are available with
reference marks as well as integrated magnetic limit switches.

Today’s changing requirements on machines such as measuring machines or production equipment in the electronics industry call for encoders that are also capable of meeting these demands. The problem of thermal expansion can be solved by the proper selection of different graduation carriers that are uniformly capable of using the same model of scanning head.  In conjunction with measuring standards of glass and glass ceramic, the new generation of LIDA 400 exposed linear encoders offer ideal properties for accurate measurement even on shop floor and in production-integrated machines.

HEIDENHAIN
www.heidenhain.com

________________________________________________________________________________________

The METALLUR process

HEIDENHAIN has developed a process—known as the METALLUR process—for manufacturing graduations on glass, glass ceramic, or steel.  The quasi-planar graduation structure provides optimum protection against contamination and thereby greatly enhances encoder reliability. The manufacturing processes are environmentally friendly and do not use chemicals such as those generally needed for etching.

Omron Electronic Components, LLC

A new highly portable mechatronic system to measure harmful pollutant relies significantly on a MEMS flow sensor

Figure 1. Stationary Aethalometers are used throughout the world, but have been too heavy to be truly portable until now.

Carbon dioxide is well known as a major contributor to global warming, and there are many ways to detect and measure it. But it is not the only culprit. Scientist have found that the second most significant contributor is soot, or black carbon. Not only does black carbon contribute to environmental degradation, but these tiny particles also cut short the lives of seniors and sicken children. A recent economic impact study in California’s San Joaquin Valley (The Benefits of Meeting Federal Clean Air Standards in the South Coast and San Joaquin Valley Air Basins, November 2008) has identified the cost of air pollution and estimated it at more than $1,600 per person per year.

Black carbon doesn’t stay in the atmosphere as long as carbon dioxide, so controlling it has the potential to achieve major benefits in the short -term. Some of the major emitters of black carbon are diesel engines plus wood- and coal- burning fires. However, to analytically determine the source of black carbon and recommend effective changes to correct the problem, scientists require instruments capable of measuring black carbon in the field.

Manufactured by Magee Scientific of Berkeley, CA, the Aethalometer, is an instrument that uses optical analysis to determine the mass concentration of black- carbon particles collected from an air stream passing through a filter. However, until recently, these instruments were too large and bulky to be easily moved to a suspected point of origination for black carbon; the smallest device (the AE42) weighed approximately 25 lbs and measured 11 x 12 x 8 in. The instruments collect data from installations located around the world (Figure 1), but these only give scientists local samplings.

To get a complete picture of the black-carbon problem, scientists required a very small portable Aethalometer to easily determine black- carbon readings in almost any location. A reduction in size required some clever engineering and component sourcing.

Figure 2. The AE51 Aethalometer’s designers took advantage of the flow sensor’s port placement by designing the manifold to interface to them directly without tubing.

Aethalometer operation

Aethalometers function by measuring the amount of particulate deposited on a fiber filter by a specific amount of air passing through the filter for a predetermined amount of time. This mechatronic system needed to incorporate mechanics, electronics, and computing in one compact package. One of the major size reduction obstacles to overcome was finding a small, lightweight, highly accurate flow sensor with low power consumption. Having worked with Omron in the past, the engineers from Magee Scientific again called on Omron for a solution to their requirements, and the company recommended its D6F-P MEMS mass flow sensor for gathering the required air samples.

Figure 3. D6F-P flow sensors are individually calibrated before shipping to deliver excellent repeatability results.

Size and power constraints

The body of the D6F-P measures just 10 mm high by 23.3 mm wide by 27.2 mm deep, and with a weight of just 8.4 grams, it fell within the size and weight restraints set forth by Magee. Designed for easy installation, the D6F-P has both the input and output ports on the same side which facilitates the connection of tubing.

Magee engineers made clever use of this feature, designing their new AE51 Aethalometer so that the sensor ports would mate directly to their manifold, without the need for tubing (Figure 2). Since this miniature Aethalometer was to be battery powered, current consumption was a concern. The D6F-P proved to be very efficient, drawing a maximum of only 15 mA while operating on 5 Vdc.

Accuracy and repeatability

The AE51 relies on calculating the exact amount of air, driven by a blower incorporated in the device for a given time. Therefore the flow sensor would have to be very accurate. The D6F-P’s flow range/ pressure range of +1.0SLM (+0.84 in H2O) with an accuracy of ±5% F.S. maximum and ±2% F.S.

typical would deliver the precise flow readings Magee required to obtain reliable measurements.

Additionally, since the sensors are individually pre-calibrated at the factory for high repeatability, Magee Scientific’s finished device adjustment and test time was kept to a minimum (Figure.3). Durability was also a concern since the AE51 would have to take multiple readings, but the sensor’s MEMS technology has been proven to deliver a long life with excellent repeatability.

Figure 4. A patented dust segregation system with dual centrifugal separators ensures that the sensing chip remains clean.

In the real world

Since the AE51 is designed to measure black- carbon particulate in areas of known high concentration rates, the sensor had to be reliable in these dirty, real- world environments. Measurements would need to be taken at busy traffic intersections, bus stops, industrial sites, and coal-burning power plants.

The AE51 would also be used in remote areas of the world where use of wood fires to cook and heat is common. Although the filter used to measure the density of the black carbon is in front of the sensor’s inlet, if any particles that got past were to effect sensor operation, measurement accuracy would be compromised.

Figure 5. The reduced size of the hand-held AE51 is obvious when compared to the rack mount AE22 Aethalometer behind it.

To prevent that occurrence, the D6F-P design uses a patented dust segregation system (DSS). The DSS in the flow path incorporates dual centrifugal chambers, in which particulate matter follows in the outer path away from the MEMS sensor chip regardless of the flow direction. Thus there is practically no degradation in sensor performance over the lifetime of the system.

Keeping the MEMS sensor chip clean lets Magee guarantee a long life for their Aethalometer without worry about black-carbon build- up harming the device’s performance (Figure 4).

The A51 Aethalometer (Figure 5) is so small that it can be strapped to a user’s belt, enabling the user to become the instrument’s legs and freeing the user to do other work while the meter is gathering information. It can also be tethered to weather balloons for upper atmosphere readings. Another potential application would allow the device to be carried by those whose health might be affected most by inhaling large amounts of black carbon. The AE51 would alert them to areas that have high concentrations of this toxic material.

Omron Electronic Components, LLC

www.components.omron.com

DLM2000 series mid-range oscilloscopes are compact, lightweight, and inexpensive. As such they present a new direction for mixed signal oscilloscopes, meeting customer needs in the increasingly digitized mechatronics and electronics fields. In the 200 MHz to 500 MHz bandwidth range, this product series delivers the highest performance in its class. An industry first, these MSOs are ideal for personal use and are being presented to the market under our new concept of “One person, one MSO.” Yokogawa aims to develop this new personal MSO market and capture the top share.

Development Background
In recent years, electronic devices and embedded systems have been built into products as varied as information appliances, automobiles, and industrial machinery. To inspect such products and analyze their performance, there is a growing need for oscilloscopes that can simultaneously measure analog and digital signals. According to our survey, approximately 70 percent of oscilloscope customers need to measure digital signals, and half of these need an oscilloscope with 8 channels or less.

However, the mixed signal oscilloscopes currently on the market are either waveform observation models without search and other analysis functions needed for software debugging or are large, expensive, and difficult-to-use high-range models for measuring ultra-high-speed signals in electronic devices. Customers therefore have no choice but to use a high-range mixed signal oscilloscope even for measuring digital signals having 8 channels or less. Yokogawa’s DLM2000 series mixed signal oscilloscopes are optimized for exactly this group of customers.

Product Features
1. Compact, lightweight, and inexpensive MSO for personal use
These compact (293 mm height x 226 mm width x 193 mm depth), lightweight (4.5 kilograms), and inexpensive MSOs are made possible by the newly developed ScopeCORE LSI engine, on which key oscilloscope technologies have embedded with a high density. This MSO is perfect for personal use.
2. Flexible MSO input
The fourth channel of these MSOs is a flexible MSO input that can be switched between analog and digital. Up to either 4 analog channels or 3 analog channels plus 8 digital channels (8-bit logic) can be input.
3. High-speed sampling and largest memory in its class
The maximum sampling rate of 2.5 GS/s is six times faster than that of the previous product series, and the maximum memory size of 125 Mpts is four times larger.
4. Intuitive, easy operations
Various menu and panel features enhance the ease of operations. These include improved waveform display on a screen that is two times larger than the previous model, the dedicated knobs according to frequency of use, and the use of eight languages in menus and panels.

Major Target Markets
Mechatronics related manufacturers in industries such as automobiles and industrial machinery Manufacturers of information appliances, AV devices, communication devices, and office equipment

Applications
Design and evaluation of electronic and electric circuits Development and debugging of electronic devices, microcomputers, and firmware on embedded devices

www.yokogawa.com

The unique abilities of the RT3 version along with the support provided by Reliance allow the scientists to concentrate on the research without having to spend time controlling and verifying the test system.

The compact closed loop motor system has unique abilities to share I/Os, perform complex coordinated motion and use mathematical notation to perform motion. The onboard memory and logic banks along with the integrated tuners, vector drive, amplifiers, 32 bit RISC processor and 50,000 count magnetic encoder provide an intelligent motor which delivers cool running and smooth motion.

For this application, the motor moves extremely slowly and has been programmed to complete a 400mm long inverse cosine motion profile over a 12 hour 24 minute period using a 0.1″ leadscrew. Using this feature the scientists are able to replicate the lunar tides found in underground oil reservoirs for their experiments.

By Mitsuhiro Nakamura
Agilent Technologies, Inc.

Recently, various devices using MEMS technology such as pressure sensors, accelerometers, and RF MEMS have been commercialized. Additionally, new devices such as silicon microphones, are rapidly evolving. The MEMS market started with the automotive industry and has been expanding to consumer products such as cellular phones.

This MEMS market expansion also applies pressure on manufacturers to lower their costs per device. However there are few opportunities for cost reduction. The limiting factors include:

• Low yields due to the precision process
• Slow throughput due to application of the physical stimulus.

A recent study (item 1 in the Appendix) estimates that 80% of the total production cost is attributed to the device packaging process and how defective chip inflow to the packaging process can contribute to cost increases. Therefore, we will discuss how to evaluate MEMS elements at the on-wafer stage in order to lower the total production cost.

Lowering Production Cost

Testing MEMS elements in the earliest stages of the manufacturing process can help contribute to lowering overall production cost. Key considerations are:

• Prompt product quality improvement by fast process feedback
• Production cost reduction by removing defective chips before package integration.

In particular, testing MEMS at the on-wafer or die level is critical for lowering mass production cost. When testing the MEMS movable part at the wafer or die level, the input and output of the MEMS device need to be considered.

Input and Output Considerations

There are two input methods that drive MEMS devices. One is to apply a physical stimulus such as pressure or acceleration, and the other is to apply an electrical signal. The movable part of sensors is also driven by the bias voltage applied. Applying an electrical signal as the input stimulus is the best method in terms of the speed, repeatability, accuracy and usability, while applying physical stimulus is better when duplicating the device’s operating behavior.

There are also two different methods to measure the output of MEMS devices. One is with a direct displacement measurement with a laser interferometer, and the other is an electrical measurement using test signals. The electrical measurement is applicable for electrostatic capacitance or piezoelectric resistance. Though the direct measurement with a laser interferometer is straight forward, the electrical measurement is superior in terms of repeatability, accuracy and usability.

Both test throughput and yield are critical in the mass production process, as are measurement speed and repeatability of the test instrumentation used. Test equipment usability should also be considered for lowering production cost because the usability affects the ease of maintenance for a test system, which determines the production line up time. Thus, electrical testing to characterize MEMS wafers or dies is preferable to physical stimulus test for lowering production cost (Figure 1).

MEMS wafer characterization process.

MEMS Capacitive Sensors

Common methods for detecting the displacement in MEMS sensors, such as pressure sensors, accelerometers, and silicon microphones, utilize piezoelectric or capacitive techniques. We will examine the electrical test of capacitive sensors as an example.

The capacitive sensor can be modeled as shown in Figure 2. The distance between electrodes is changed by the physical stimulus such as pressure, acceleration, or sound waves. The change in distance can be read electrically as the electrostatic capacitance change.

Capacitive sensor diagram.

The capacitive sensor has two mechanical characteristics — the static response (static characteristic) and the dynamic response (dynamic performance) against the input of the physical stimulus. Static response is a fundamental characteristic and is defined as the capacitive sensor displacement when a static physical stimulus is applied. The dynamic performance is described as the response when a dynamic physical stimulus is input. The dynamic performance is expressed as the frequency response of amplitude and phase, often represented by parameters such as resonance frequency, Q-factor, and 3 dB bandwidth.

Evaluation by Electrical Measurement

As previously mentioned, the physical stimulus input can be replaced with the electrical stimulus input, which we will now examine.

The static physical input can be replaced by applying DC voltage bias. Therefore, the static characteristic can be evaluated by measuring the electrostatic capacitance by sweeping the DC bias voltage. This measurement is generally referred to as a Capacitance-Voltage (C-V) measurement. The important specifications and features of the test instrumentation are impedance range, measurement accuracy, frequency range, measurement speed, repeatability, and DC bias voltage range.

The capacitance of the most capacitive sensor is approximately 0.5 to 1 pF at the neutral position. Consequently, the test equipment needs to be capable of measuring electrostatic capacitance accurately in the order of 0.1 pF. The four-terminal pair method is recommended for the most accurate impedance measurement.

Note that an AC test signal is used for the impedance measurement. If the test frequency is set as low as the electrodes of the device, the voltage of the test signal can actuate the electrodes. When the electrodes are moving, electrostatic capacitance cannot be measured correctly. Therefore, the test frequency should be set much higher than the mechanical operating frequency of the device under test. Generally, the operating frequency of the MEMS device is in the low kHz range, so a 1 MHz test frequency is adequate.

As superior impedance measurement repeatability enables the tightening of the guard band in the testing process, it also helps to improve yields. Measurement repeatability is important when characterizing small mechanical displacements, as is device processing accuracy. In the case of a capacitive sensor with capacitance of 1 pF, the measurement repeatability should be less than 0.1%, which means that 1 fF or less of the repeatability is recommended.

Caution must be exercised when the DC voltage bias sweep measurement is performed. Capacitive sensors have hysteresis characteristics based on the amount of electrical charge being inducted to its electrodes. This hysteresis is one of the parameters to be evaluated by a C-V measurement.

Precision LCR meters are generally used for these types of measurements.

Dynamic Performance

The dynamic physical stimulus can be replaced with the AC voltage applied by the electrical measurement. The characteristic of the movable part as a portion of the electrode can be modeled as shown in Figure 3. The mechanical characteristic of the movable part is reflected to the measured impedance at a lower test frequency than the mechanical operating frequency. Thus, measuring the impedance of the device can illustrate the frequency response of the movable part. The four-terminal pair method with the impedance measurement is recommended to achieve the most accurate measurement.

The movable part being driven by the AC voltage applied has electrostatic attraction between electrodes. Because the electrostatic attraction is proportional to the square of AC voltage applied, it generates a second distortion to the current flowing into the electrodes.

An impedance analyzer obtains the impedance value by the measured vector value of the fundamental element of voltage over that of the current, so the second distortion may cause measurement error. Applying DC bias voltage to the AC test signal is a good way to solve this problem. When the amplitude of the AC voltage is adequately smaller than that of DC bias voltage, the second distortion of the current is negligible so that a valid measurement can be performed. This method allows for a quick and easy evaluation of the frequency response of the device, except when the electrode is at its neutral position.

Impedance versus frequency profile is the fundamental measurement for characterizing the dynamic performance of the device and can be obtained with an impedance analyzer. The dynamic performance of the device can be derived from the measurement results, which represents the performance at the position of the electrodes driven when a DC bias voltage is applied. The dynamic performance at any position can be obtained by varying the DC bias voltage. The level of AC test voltage needs to be set smaller than that of DC bias voltage.

Note that the measured impedance profile has both impedance, representing the dynamic performance, and the electrostatic capacitance, representing the electrode displacement. An equivalent circuit model is shown in Figure 3. To determine the dynamic performance of the device itself, electrostatic capacitance can be subtracted. The electrostatic capacitance can be obtained from the measured impedance value at a higher test frequency than the operating frequency of the device.

Characteristic of the movable part of the electrode.

Leakage Measurement

Besides characterizing static and dynamic performance of MEMS devices, leakage measurement is also an effective consideration for quality management. The leakage measurement between the electrodes enables the early detection of device defects.

A pico ammeter such as a semiconductor device analyzer or high-resistance meter is generally used. If parametric testing is required due to monolithic-type MEMS devices containing transistors and MEMS elements in one chip (e.g. at the die level), a semiconductor test system can be used.

However, a high-resistance meter can be sufficient for leakage test in terms of cost, simplicity, and quick operation. The required performance for the MEMS leakage test is that an instrument has the ability to measure the resistance at 100 GΩ or more accurately.

Setup and Configuration

For on-wafer measurements, configuring the probe station and probe card with the test instrument also need to be considered. The shape of the probe card depends on the device under test. However, for the precise measurement by the four-terminal pair method, the cabling from the device to the probe and also the card design are important.

The impedance measurement requires the ability to compensate for the measurement errors caused by cable extension and the parasitic impedance of the probe card from the measurement data. Compensation has to be performed at the end of the probe, using supplied impedance standard substrates from the probe station vendor.

The above considerations and compensation procedure are similar to that of a FET gate insulator measurement. For additional information, refer to items 2 and 3 in the Appendix.

Conclusion

As we have discussed, making on-wafer impedance measurements at the earliest stages in the manufacturing process can be very effective in lowering the production cost of MEMS devices. Using high-performance test instruments with impedance measurement techniques that are accurate, fast, and repeatable are required to characterize the small mechanical displacements of these MEMS devices.

Agilent Technologies, Inc. www.agilent.com

[edit] Appendix

1. . The MEMS Test Community — http://www.memunity.org/on-wafer_testing.htm
2. . Application Note: “Agilent Evaluation of MOS Capacitor Oxide C-V Characteristics Using the Agilent 4294A,” Literature Number 5988-5102EN.
3. . Application note: “Agilent Technologies Impedance Measurement Handbook,” Literature Number 5950-3000.

By Sugato Deb, Ph.D., MBA & Director Emerging Markets / Partnerships
National Instruments

In the traditional design process of parts and assemblies, engineers produce models, analyze their behaviors under operating conditions, and pass physical prototypes “over the wall” for test engineers to evaluate in a pass-fail mode. Any problems that come to light are “thrown back” for design changes that, though necessary, come at the cost of additional prototypes and development time.

If that wall could be broken down, with analysis and testing working together in a closed-loop cycle, both groups would reap benefits from the use of test-based input values to drive analysis models, the use of analysis results to recommend sensor locations and test scenarios, and faster and better product development cycles.

Typically, once you have created a solid model of a part or assembly, the next steps are to define boundary and operating conditions, then perform a finite element analysis (FEA) to identify the behavior of the part in response to those conditions. Influencing the accuracy of such analyses are the mathematical algorithms and actual coding of the analysis software, and the assumptions made throughout the problem definition process, whether based on geometry or physics. Factors that influence these areas include available computing power, especially at the desktop level, and refinements in FEA algorithms in the newer analysis software packages. Thus, accuracy now stems from assumptions made in determining material properties; boundary conditions; geometry idealization; and physics simplification, such as flexible versus rigid behavior and linear versus non-linear behavior. Ideally, you would have continually improving sources of data on which to base the values for such input conditions.

Factors in mechanical test procedures

Figure 1. To record the shape of the vibration response in this hollow aluminum 50-cm diameter wheel in the shape of the Euro symbol, accelerometers were attached around the rim and parallel bars, and connected to dynamic signal acquisition devices on a PXI (PCI eXtensions for Instrumentation) platform.

Test set-ups for a mechanical part or assembly involve best practices, years of experience, flexible hardware measurement systems, test and control software and input from the actual mechanical designer. Typically, testing takes a pass/fail approach, verifying failure at some maximum load value or confirming in-spec temperatures at locations throughout a part. If the measured values don’t match the predictions, it’s back to the drawing board with revisions and more tests. However, it can be difficult to tell if the test itself generated inaccurate data, since the following experimental parameters can lead to errors: sensor locations, sensor and system calibration, sensor adhesion, sensor mass loading, test fixturing (free-free or constrained), excitation or loading locations, and load cycle. With better sources of specific information for choosing these variables, the test results would be more reliable and provide better feedback to verify and improve the analyses.

Differences in tests and analyses

Figure 2. Engineers analyzed this structure in the constrained mode for the natural frequency response in CosmosWorks.

Analyses results can be viewed on the original 3D CAD models. They can be displayed as color maps representing small changes where you can rotate, zoom, and select any point, then read its corresponding value (such as stress) across the model.

In the testing world, it’s not as easy to look at the results and draw conclusions about physical behavior. For example, the output of a series of strain gauges is a stream of data, plotted as a set of superimposed curves on an x-y graph, with each curve tracking the measured values from a single sensor over time. An experienced viewer can pick out significant peak values or identify a trend of measurements from a sub-set of physically clustered sensors. However, it’s still a challenge to sort out a hundred or a thousand sensors, and track them back to their corresponding locations on the physical model to fully understand their relevance.

What if the test results could directly, point-by-point, help calibrate and verify the approach to the analysis? You could compare an analysis with the test values to see when and where they differed. If a subset of values were quite off the mark, this might indicate that a nonlinear instead of linear analysis would provide a more accurate approach.

Conversely, what if the analysis could help test engineers determine the best locations for sensors and decide where and how to place the loads? Overlaying test locations on a stress distribution model would better support decisions of where to place the sensors – targeting key expected stress points – instead of attaching them in a simple grid pattern that might miss local areas of unusual activity.

Bringing in test data

To integrate test with design and analysis, four types of disparate information must be correlated: the 3D part geometry from the FEA mesh or the CAD model, analysis data, the physical location of each sensor, and the measured values taken from each sensor over time. Test data are usually sparse as they come from discrete sensor locations while FEA data are integrated over millions of individual elements. It would be useful to interpolate between the sensors to generate test values for every physical point on the model at a resolution comparable to that of an FEA mesh. Then a color-shaded image would let you “see” the test data in the same graphic style as the analysis results, overlaid on the exact geometry, with animations showing behavior over time.

Since every node on an FEA mesh can have a calculated and a measured value, correlated data sets would also let you generate error-map images comparing both values. Here is an example of a project that mapped these requirements into a common view, based on integrating software from SolidWorks (SolidWorks 3D CAD and Cosmos Analysis software) and National Instruments (NI).

Vibration Modeshape

Figure 3. Mapping test data to the geometry and deforming it let the engineers see the test mode shape without a high-speed camera. The differences between test and analysis were displayed in the same view, along with a simple camera-image of the device under test for comparison. The views could be used to calibrate and improve the analysis prediction.

Modal frequencies and mode shapes are often evaluated for structures operating in a dynamic environment such as an automobile or in industrial machinery. The main concern is that the structure may vibrate excessively, causing it or other parts to fail prematurely. Vibrations may also transmit to other parts of the structure affecting the perceived quality of the system. The historical challenge in vibration testing is that in addition to requiring expensive measurement systems with high accuracy (24 bit) and high sampling rates (greater than 100k samples per second), the short dynamic nature of the event requires synchronized and sampled measurements at all the sensors (accelerometers).

Another issue is where to place the sensors. A sensor placed at a primary system node will register zero displacement and acceleration. Use of a force hammer and a trial and error process to excite the structure to capture all the mode shapes can also result in inaccurate data. Often the test engineer does not know whether the tests have been successful until all the data are analyzed off-line, possibly several days later. If the mode shapes have not been sufficiently captured, the tests need to be redone. Lastly, the test design must account for mass loading from the accelerometers, since this factor can often distort the test results for light or hollow structures. Usually, the density of sensors is sequentially reduced to limit the effect; unfortunately, this also decreases the amount of captured test data.

In the example, the unit under test was a hollow aluminum 50-cm diameter wheel in the shape of the Euro symbol. The structure was fixed at two locations but otherwise free to vibrate. To record the shape of the vibration response, accelerometers were attached around the rim and parallel bars, then connected to the appropriate NI dynamic signal acquisition (DSA) devices on the PXI (PCI eXtensions for Instrumentation) platform (figure 1).

Engineers analyzed the same structure in the identical constrained mode for the natural frequency response in CosmosWorks (figure 2).

An instrumented force hammer was used to excite the structure at the free end of the shorter straight cross-bar; the response at all the accelerometers was recorded over 100 milliseconds, at a sampling rate of 10,000 Hz, until the vibrations had died down. The accelerometer data was recorded and analyzed by NI LabVIEW Sound and Vibration Toolset and transformed from the time domain to frequency domain for easier analysis.

The resulting mode shape was brought up in NI Insight, side-by-side with the CosmosWorks analysis results and the comparable normalized test values interpolated from the sensors. The animation option generated the mode shape. The ability to map test data to the geometry and deform it accordingly let the engineers see the test mode shape, a task that would otherwise require a high-speed camera. The differences between test and analysis were displayed in the same view, along with a simple camera-image of the device under test for comparison (figure 3), which could be used to calibrate and improve the analysis prediction.

The analysis results helped guide the test engineers to optimize the sensor locations and change the placement of the excitation strike.

For sensor mass loading, you can model the accelerometer masses in the analysis, and then calibrate the mass-loaded analysis with the similar mass-loaded physical test results to improve the analysis fidelity. Then, the accelerometer masses can be unloaded in the analysis (which is not possible in the physical world) and the true modal frequency and mode shape predicted without mass loading.

This approach is only possible by integrating the analysis with the physical test; neither analysis nor physical test alone can accomplish the task, which further points to the real value that integration brings to the table.

Validation for integrated motion control

Another area that could benefit from feedback between software analysis and actual testing is control-system design, whether in mechanics, thermal, or fluid-solid systems. Today’s high-speed electromechanical systems often include a servo-driven actuator that must operate with microsecond response times. Incorrect motion control configuration settings such as Proportional, Integral and Derivative (PID) gain parameters can lead to large settling time or excessive over- or undershoot.

If the motion dynamics of the plant could be analyzed, accounting for forces, friction, gravity, mass or thermal inertia, the information could be fed back to the controller analysis to improve motion dynamics. This design validation capability exists through the combination of CosmosMotion dynamics analysis software and NI LabVIEW Control Design along with the NI SoftMotion Development Module software for motion controller analysis. CosmosMotion helps simulate mechanism motion by taking into account mechanism dynamics, such as forces and friction, and generates such information as position and kinetic energy.

NI LabVIEW with NI SoftMotion help simulate a custom motion controller with functions such as trajectory generation, spline interpolation and control algorithms such as PID. The first round of control parameters calculated in NI LabVIEW is fed back to CosmosMotion to verify how the plant will react to that stimulus, and, depending on how large the feedback error is, the control parameters are tuned until acceptable system performance is reached.

Such closed-loop analysis between mechanical motion and control development environments can help drive design decisions for both the mechanical and controller aspects of the design. For example, engineers may choose to replace a ball-screw stage with a linear motor when they discover the given load cannot be moved at the rate they want.

Using analysis results to refine tests, and using test data to improve analysis models, offers a win-win approach to increasing company-wide productivity and gaining a competitive advantage in the marketplace.

National Instruments Corp. (www.ni.com)