Sensor signal-conditioning ICs ease the design of sensor systems

 The market for sensors and sensor-related components is a high-growth industry expected to expand in automotive, industrial, medical, and consumer applications. Products such as media players, tablet PCs, and smartphones are driving significant growth in the sensor market, requiring a related increase in the number of designers and manufacturers integrating sensors into modules for resale or for their own products. The wide range of sensing element types and demands for faster time to market and lower costs present numerous challenges, even for veterans of sensor design. 

 The perennial challenge for sensor interface designers is correcting and calibrating the inherent non-idealities present in transducers, typically offset and nonlinear response to stimulus with a temperature dependence for one or both of these factors. There are a host of custom design approaches and solutions to this problem, but the availability of commodity integrated circuits offers designers new choices that are powerful and cost-effective. By combining precise, programmable analog circuitry with high-density digital controllers dedicated to processing correction algorithms, these sensor-signal-conditioner (SSC) ICs reduce the design time and cost of sensor systems while providing the designer with a menu of built-in capabilities and support tools for implementing sensor correction. Understanding the sensor’s characteristics and how to configure its corresponding SSC are key ingredients for obtaining optimum performance and keeping costs low.

 Overview of sensor correction

Transducers exhibit various types and degrees of offset and nonlinear response. The basic idea of calibration and correction is to maximize the usable range and transform the nonlinear response into a predictable linear output that minimizes the error in the sensor output. The nature of non-idealities varies widely between sensor types, and the difficulty and complexity of applying corrections increase in proportion to the magnitude and degree of these undesirable effects.

Fig. 1: Typical sensor responses to input stimulus.

Figure 1 illustrates several types of sensor responses. Each has different basic characteristics and related correction issues. S1 has low offset and relatively low nonlinearity. S2 has a narrow span but a very high offset, which must be removed before applying sufficient gain to create a useful signal level. S3 has a sharp “knee,” and piecewise linear correction is generally a good option for these types of nonlinearities. S4 has an inflection point and would require at least a third-order polynomial correction to achieve a high accuracy over the entire measurement range.

 Another important factor to consider is how these sensors behave over temperature. Figure 2 shows a typical scenario for the temperature variation of a sensor element. In this case, the offset increases while the span decreases with increasing temperature. The challenge is to understand what the exact nature of the dependence is and remove its contribution to system error. Plotting the offset and gain versus temperature will reveal another set of curves with linear, quadratic, or higher-order dependence on temperature.

Fig. 2:  Sensor output variation over temperature.

Each individual sensor element will have its own characteristic span and offset with respective temperature dependencies. The type of correction algorithm applied must also account for the type and degree of these differences across variations such as process tolerances, shifts between manufacturing lots, or package stress effects introduced in the next assembly level.

Hardware implementation

The block diagram shown in Fig. 3 presents a practical and cost-effective approach to sensor calibration and correction. It is a 16-bit resolution resistive-bridge sensor signal conditioner with built-in correction algorithms capable of compensating for a variety of undesirable sensor characteristics. A proprietary microcontroller with 18-bit digital signal processing (DSP) performs the necessary calculations for the correction algorithms using calibration coefficients stored in nonvolatile memory. In addition, this device performs auxiliary operations including temperature sensing and bridge biasing, and it has multiple communication interfaces. It represents a complete solution for interfacing and correcting the output of a sensor bridge, providing a precise, accurate, and compensated sensor output.

Fig. 3: Block diagram of a sensor signal conditioner IC.

Getting to know your sensor

One of the most important and effective tasks that the designer can carry out is a thorough characterization of the sensor element. Time and effort invested in this important step will pay off in the long run by reducing overall design time and development costs, improving the overall system performance and robustness, and ultimately reducing production test time and cost. It is tempting to rush through this part of the product development cycle, but experienced sensor system designers will testify to the importance of spending the necessary time and resources to characterize and analyze sensor data before proceeding to the next step of developing an optimized sensor correction algorithm.

For example, consider the response curve of sensor S3 in Fig. 1. If the input range is limited to between 10% and 30% or 60% and 90%, a first-order gain and offset correction algorithm might suffice, depending on temperature variations. However, if the sensor must operate across the entire sensor input range, a more sophisticated correction algorithm is needed. Even if the intended range of operation appears to be confined to one of the linear regions, consider what would happen if a future lot of sensors were to shift so that the knee of the curve moved into what was previously a linear region? Not having the flexibility and availability of more sophisticated correction techniques could require significant redesign.

 It is vitally important for the sensor system designer to understand the characteristics of the sensor across the input measurement range and over the operating temperature range.

 Some of the more important considerations include the more important considerations include 

• The shape and order of the sensor response over the desired measurement range, including at least a 10% margin outside the expected minimum and maximum values.
• The type and order of temperature dependence for offset and span.
• The consistency of the measured parameters. Consider what would be the effect on the correction algorithm if future manufacturing lots have a shift in a significant feature such as an inflection point or the sign of a temperature coefficient.
• Whether the characterization data set is adequate and statistically significant. This includes the number of devices tested and the number of points measured for each.
• How much error the data acquisition system contributes to the characterization.

Selecting and implementing the best correction technique

Once the sensors have been characterized and the dataset is evaluated, the next step is to narrow the field of correction options. The ultimate goal is to produce measurement results that meet sensor product accuracy requirements with the minimum number of points necessary for calibration during production. With the sensor characterization data in hand, the degree and type of correction required for gain and offset can be matched with the best algorithm available in the SSC.

 Table 1 is a list of the some typical algorithms available in commercial ICs. The algorithms are organized by the type and degree of correction, and the second column indicates how many measurement points are needed to calculate the calibration coefficients for each algorithm. The next columns list the element of correction each calibration method applies and describe the sensor characteristics that must be isolated and quantified to determine the optimal algorithm. TC refers to the temperature coefficient. Eliminate algorithms that correct for negligible effects in the particular system and choose the one with the least number of measurement points.

Table 1: List of correction algorithms for an SSC showing the number of calibration points and the correction factors applied. 

 SSC manufacturers usually provide hardware and software for their devices that allow selecting and evaluating the calibration methods quickly and easily. Software installed on a PC guides the designer through the process of data acquisition, recommending which measurements to make and selecting the best calibration method based on measurement results and system requirements.

Fig. 4: Screen capture of software aid for selecting and evaluating calibration methods.

Figure 4 is an example of development support software for an SSC showing the data points needed for a four-point calibration routine. After collecting data from the user about the input and temperature ranges, the software guides the user through a series of measurements for any of the calibration methods selected from Table 1. Once all data have been collected, the software calculates the necessary coefficients for the selected algorithm and programs them into the SSC device. The SSC and its matched sensor element are paired from this point forward, resulting in a calibrated sensor system.

Hardware and software support like this is very valuable to the designer, facilitating rapid development and optimization of a sensor interface without requiring intimate knowledge of the built-in algorithms or writing software to solve messy equations to calculate and evaluate polynomial coefficients. When selecting a sensor interface IC, the designer must look at datasheet parameters including voltage and temperature ranges, ADC resolution, and noise levels, etc., but the power, efficiency, and flexibility of correction algorithms combined with the level of expert knowledge and development support provided by the manufacturer are just as important as the specifications.

Summary and conclusions

Sensor system design is challenging, and there are a wide range of solutions and products to consider. Sensor-signal-conditioning ICs are powerful tools for designing sensor systems, and they contain the cumulative knowledge of decades of ingenuity and experience. They are cost effective and power efficient, yet they deliver high precision and accuracy if implemented properly. However, they are also complex and require auxiliary tools and support to achieve optimal performance.

The author  

  David Grice, Field Application Engineer of ZMD America Inc.