AFS090 datasheet(117/334 Pages) MICROSEMI

117 / 334 page

Fusion Family of Mixed Signal FPGAs

R e visio n 2

2 - 101

This process results in a binary approximation of VIN. Generally, there is a fixed interval T, the sampling

period, between the samples. The inverse of the sampling period is often referred to as the sampling

frequency fS = 1 / T. The combined effect is illustrated in Figure 2-80.

Figure 2-80 demonstrates that if the signal changes faster than the sampling rate can accommodate, or if

the actual value of VIN falls between counts in the result, this information is lost during the conversion.

There are several techniques that can be used to address these issues.

First, the sampling rate must be chosen to provide enough samples to adequately represent the input

signal. Based on the Nyquist-Shannon Sampling Theorem, the minimum sampling rate must be at least

twice the frequency of the highest frequency component in the target signal (Nyquist Frequency). For

example, to recreate the frequency content of an audio signal with up to 22 KHz bandwidth, the user

must sample it at a minimum of 44 ksps. However, as shown in Figure 2-80, significant post-processing

of the data is required to interpolate the value of the waveform during the time between each sample.

Similarly, to re-create the amplitude variation of a signal, the signal must be sampled with adequate

resolution. Continuing with the audio example, the dynamic range of the human ear (the ratio of the

amplitude of the threshold of hearing to the threshold of pain) is generally accepted to be 135 dB, and the

dynamic range of a typical symphony orchestra performance is around 85 dB. Most commercial

recording media provide about 96 dB of dynamic range using 16-bit sample resolution. But 16-bit fidelity

does not necessarily mean that you need a 16-bit ADC. As long as the input is sampled at or above the

Nyquist Frequency, post-processing techniques can be used to interpolate intermediate values and

reconstruct the original input signal to within desired tolerances.

If sophisticated digital signal processing (DSP) capabilities are available, the best results are obtained by

implementing a reconstruction filter, which is used to interpolate many intermediate values with higher

resolution than the original data. Interpolating many intermediate values increases the effective number

of samples, and higher resolution increases the effective number of bits in the sample. In many cases,

however, it is not cost-effective or necessary to implement such a sophisticated reconstruction algorithm.

For applications that do not require extremely fine reproduction of the input signal, alternative methods

can enhance digital sampling results with relatively simple post-processing. The details of such

techniques are out of the scope of this chapter; refer to the Improving ADC Results through

Oversampling and Post-Processing of Data

white paper for more information.

Figure 2-80 • Conversion Example

LSB

AFS090 Datasheet(PDF) 117 Page - Microsemi Corporation