In-depth analysis: three types of analog front-end analog/digital converters

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In-depth analysis: three types of analog front-end analog/digital converters [Copy link]

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画中画广告结束The three most common types of A/D converters in analog front ends are successive approximation (a), pipeline (b), and delta-sigma (c).

The following block diagrams provide a very simplified description of these different architectural concepts.

a. In a successive approximation analog-to-digital converter, the analog input voltage is "frozen" by a sample-and-hold technique. Then, an N-bit register is set to mid-scale mode: the highest bit of the register is set to 1, so that the digital-to-analog converter output can reach mid-scale values.

If the input voltage is higher than the D/A converter output voltage, the comparator output is true and the highest bit remains at 1. However, if the input voltage is lower than the D/A converter output voltage, the highest bit of the register becomes a logic 0.

The converter control logic will go to the next bit, drive that bit high and perform another comparison until it reaches the least significant bit. When the conversion is complete, the N-bit number will appear in the register. See the related illustration

b. In a pipelined A/D converter, each parallel stage samples one bit at a time or multiple bits at a time. The analog input is applied to a sample and hold, and the first stage A/D converter converts it to 3 bits. It then inputs a small D/A converter, which produces an analog output from the sample and hold output value. The "residual signal" is amplified and then input to the next stage, and so on. The shift register adjusts the bit values of each stage in time and passes the combined sample to the error correction logic. See related illustrations

c. Delta-sigma converters are not simply used for direct time domain analysis, but more often for frequency domain analysis. (A detailed mathematical analysis is described in Intersil Applications Note 9504 at www.intersil.com/data/an/an9504.pdf.) It can be said that roughly oversampling the input signal (sampling the target well above the Nyquist value in order to obtain the maximum input frequency band of interest) eliminates aliasing. More importantly, it spreads the frequency components of quantization noise (the resolution error caused by converting a continuous input signal into a series of non-continuous signals) over a larger bandwidth. This reduces the average level of quantization noise and increases the frequency of most noise. Most noise can be eliminated by rapidly attenuating the frequency band of interest of the digital filter. See the related illustrations

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