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Understanding sensor data acquisition

Posted: 03 Jan 2005 ?? ?Print Version ?Bookmark and Share

Keywords:sensor? microcontroller? mcu? analog-to-digital converter? adc?

Sensors are in widespread use, either as part of a microcontroller or separately in an embedded system. However, one of the more efficient circuit implementations combines a high-performance analog-to-digital (A/D) converter with an integrated MCU.

Most sensors require signal conditioning to remove potential sources of measurement error. These error sources might include gain and offset error, ambient or intrinsic noise and nonlinearity in the sensor transfer function itself. Many modern applications also require the conditioned sensor data in digital format. For systems with multiple sensors, the overhead associated with individual conditioning and conversion circuits at each sensor node can be problematic, adding maintenance, calibration and cost overhead to the end application.

Regardless of type, whether it is a thermocouple, resistance temperature detector, load cell or magnetic field sensor, sensors typically require an excitation (bias) source for signal generation and conditioning circuitry to compensate for measurement error from a number of sources.

Although these sensors measure such different parameters as temperature, weight and magnetic field strength, they share the common electrical characteristics of low signal gain, amplitude and nonlinearity. Once installed in the end application, gain and offset errors may also become evident, mandating calibration. Many sensors exhibit high (or variable) output impedance, making them prone to signal loading and coupled noise problems. These factors, taken separately or in combination, can add significant measurement error. As a result, sensors require dedicated signal-conditioning circuitry for error compensation, filtering and buffering and analog-to-digital conversion prior to use by the system.

Converter topologies

Wide dynamic measurement range is another common characteristic of many sensor applications. For example, consider a truck scale where the weight of the cargo must be measured to within 0.1lb. A practical weighing technique consists of weighing the loaded truck and subtracting the known (constant) weight of the empty truck. A suitable truck scale with a 25,000lb full scale must have 19 bits of ADC resolution to meet the 0.1lb resolution requirement. Other examples of the same dynamic-range requirement can be found in sensors used in gas detection and high-precision compass systems. Taking into account these various requirements and factoring in the probable need for wide dynamic range, the sensor ADC is in reality an ADC system.

There are many ADC topologies to choose from, including successive-approximation converters (SARs), pipelined converters, flash converters and slope converters. However, the requirement for high resolution and high signal-to-noise ratio limits the selection to an integrating ADC.

Integrating ADCs average the measurement process over many samples (or over a long time period), thereby increasing resolution while attenuating noise. Although integrating ADCs are much slower than nonintegrating types, they are more than fast enough for typical sensor applications. Examples of integrating ADC architectures are delta-sigma, dual and multislope integrators and oversampled SAR ADCs. Of these, the delta-sigma converters have become the most popular for sensor data acquisition applications because of their optimal resolution, linearity, noise performance, cost and operating power.

The delta-sigma type

The delta-sigma ADC is an oversampling converter, but oversampling is only one of the mechanisms that contributes to the converter's overall performance. A delta-sigma converter combines oversampling with noise shaping and digital filtering for the best possible resolution and noise attenuation. It is also almost entirely digital, allowing it to enjoy the scaled size and economies of modern digital electronics. This translates to small size and low installed costboth important factors in most sensor interface applications.

The delta-sigma ADC architecture consists of a modulator and digital filter/decimator. The modulator is composed of an integrator, clocked comparator and 1bit D/A converter (DAC). The integrator averages the difference between the time-averaged DAC output and VIN and applies the result to the comparator input. When clocked, the comparator generates a 1 when its input is greater than 0, which generates a +VREF pulse at the negative input of the summer. This process continues at a rate equal to the sampling clock frequency and results in a continuous bit stream of 1s and 0s. In this bit stream, the ratio of the number of 1s to the total number of bits over a given time period (i.e., 1s density) is equal to the ratio of VIN to VREF. The averaging action of the integrator also acts as a low-pass filter on the difference signal, reducing quantization noise at low input frequencies and reshaping the noise by pushing it out of the frequency range where measurements take place.

The digital filter then removes the reshaped quantization noise while the decimator computes a weighted moving average of the modulator data to complete the conversion process. However, when a delta-sigma converter is multiplexed, the data within the decimator and filter must be flushed (cleared) to prevent signal crosstalk between channels. The time to flush and refill the filter/decimator can be considerable and is commonly referred to as latency or "group delay." Many systems have a critical time budget for analog data conversion that can be adversely affected by latency. There are suppliers that offer reduced-latency delta-sigma converters that trade some degree of noise performance for the absence of latency.

Because of their high resolution and noise attenuation, delta-sigma ADCs have become popular in dc and low-frequency applications like sensor data conversion and are available from a number of semiconductor suppliers.

Strategy

A sensor ADC may be installed locally or remotely to the sensor, and circuitry may be implemented in a number of ways. Distributed systems with local conditioning at each sensor site (IEEE 1451) have become popular in recent years. One of the more efficient circuit implementations uses a "smart ADC," which consists of a high-performance ADC with an integrated MCU. Examples to date can squeeze together a complete 24bit delta-sigma ADC system with analog and digital peripherals and a 50Mips ISP flash-based 8051 MCU in a single 5-by-5mm package. With various hardware connections included and the appropriate firmware to implement linearization, calibration and serial communication, all aspects of sensor conditioning and data conversion can be performed.

System firmware

The functions provided by the MCU vary with the end application. These functions may involve measurement, control, diagnostics or other functions. Most applications require measurement (including calibration and linearization), typically data formatting and some form of communication interface.

While gain and offset calibration are performed by dedicated on-board hardware, linearization must be performed in firmware. The classic approach to linearization uses a power series polynomial equation.

Pulling it together

Other peripherals on board an ADC/MCU combination IC can include serial ports, timers and analog comparators to extend system capability beyond sensor signal conditioning and conversion. For example, on-board serial ports enable communication with other system processors for remote configuration, control and data exchange. The combination of serial communication and in-system programmable flash memory enables easy system firmware upgrades as system requirements change. Other system benefits such as intelligent power management and sensor diagnostics can be realized. All of these strategies reduce the cost of system ownership and improve system availability and performance.

- Don Alfano

MCU Applications Engineering Director

Silicon Laboratories Inc.





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