IEEE Solid-State Circuits Magazine - Summer 2015 - 48

From a technical perspective, power consumption
is often taken as the cost dimension for converter
performance metrics.

it as a doorway. Data converters
have received a great deal of attention over the decades because this
doorway has often been a critical bottleneck in determining how
much signal information can move
between the domains. Taken in
this light, it is natural to refer to
Shannon's theory suggesting that

the theoretical limit of a channel's
information capacity is a function
of its dynamic range and its bandwidth [9]. In essence, these are the
two dimensions of the doorway, or
what we will refer to here as "Shannon's box."
In fact, we can consider bandwidth and dynamic range to be the

two fundamental dimensions of any
signal processing problem, whether
audio, imaging, communications, or
sensor signal conditioning. While
a bewildering variety of specifications can be used to describe data
converters (see "Converter Specifications and Domains"), the two
most basic specifications describe
the theoretical limits of a "perfect" data converter in these two
dimensions: the sample rate sets
the fundamental limit of the signal
bandwidth (based on the Nyquist
theorem), and the resolution of the
converter sets the fundamental
limit on the signal's dynamic range

ConveRTeR SPeCifiCaTionS anD DomainS
We recognize that a data converter's fundamental dimensions of performance are its "usable bandwidth" and "effective dynamic range." The
most obvious representations of these two dimensions are the sampling
rate and resolution, respectively. However, as data converter technology
has evolved and been adapted to a number of different applications,
dozens of different specifications have been used to try to capture the
effective performance of a converter in a particular context.
One of the first things to consider is what domain the converter should
be specified in. By their nature, data converters operate as the gateway
between the analog and digital domains, but they also move information
across the discrete-time and continuous-time domains. Beyond that, we
can look at the output signal in a number of ways:
* Transfer function from input to output. How accurate and
linear is the output based on the changing input [offset error, gain
error, integral nonlinearity (INL), differential nonlinearity (DNL),
and noise]?
* Time domain. Looking at the signal with an oscilloscope, how
quickly can the output capture/track the input (settling time, acquisition time)?
* Frequency domain. Looking at the signal with a spectrum analyzer, what are the signal-to-noise ratio, total harmonic distortion,

spurious free dynamic range, etc.?
Each application may have its own sensitivities to different specifications
as summarized in Table S1, and this may ultimately drive the converters to
be optimized in different ways (or even develop particular architectures).
For example, because of the nature of the human eye, imaging applications tend to be very demanding of the converter's DNL, while relatively
less sensitive to INL errors. (The human eye can pick out "edges.") Conversely, audio systems are much more concerned with INL/total harmonic
distortion, because the human ear is more sensitive to those sorts of errors.
In any simplified model, one can map the error from one domain
into another: an INL error of a given size and shape will produce a
predictable amount of distortion. In high performance systems, these
simplified models may give false predictions-subtle design differences
may have a big impact on system performance. The problem can be
exacerbated by "specsmanship": when a given spec becomes the focus
of commercial claims, there can be a tendency towards enhancing this
spec at the cost of true performance. Examples of additional resolution
beyond the real useful performance of a data converter led to the term
"marketing bits." The desire to divine the real system performance of a
data converter leads to ever-longer data sheets, and the ongoing search
for the "best" converter specifications.

TABLE S1: A LiST of diffErEnT AppLicATionS And SomE of ThEir fAvoriTE convErTEr SpEcificATionS.

48

AppLicATion

fAvorEd SpEcificATionS

Weigh scale

Offset error, gain error, INL, monotonicity

Composite video

Differential gain error, differential phase error, DNL

Digital imaging

DNL, input referred noise, settling time

Digital audio

Total harmonic distortion specified at different amplitudes

Cellular infrastructure

Spurious free dynamic range), third-order intermodulation distortion,
signal-to-noise ratio

s u m m e r 2 0 15

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