ADC noise measurement
methods & parameters
TI Precision Labs – ADCs
Created by Chris Hall & Bryan Lizon
Presented by Alex Smith
1
Choosing an ADC
Parameter
Specifications
Noise Target
150 nV
Input Range
0 – 10 mV
Sample Rate
50 SPS
System Resolution
16 bits
Can I use a 16-bit ADC?
System Designer
2
Types of noise measurements
Sinewave-Input Test
DC-Input Test
VDD
AINP
Single-Ended
Input
VIN
+
_
ADC
AINM
VDD
VDD
ADC
AINM
AINP
VIN
ADC
+
_
Differential
Input
AINP
AINM
3
Noise plots in an ADC’s datasheet
FFT (ADS127L01)
DC Histogram (ADS127L01)
𝑺𝒊𝒈𝒏𝒂𝒍
𝑫𝒊𝒔𝒕𝒐𝒓𝒕𝒊𝒐𝒏
𝑵𝒐𝒊𝒔𝒆
𝑺𝒊𝒈𝒏𝒂𝒍
𝑵𝒐𝒊𝒔𝒆
𝑺𝒊𝒈𝒏𝒂𝒍
𝑻𝑯𝑫 = 𝟐𝟎 × 𝐥𝐨𝐠
𝑫𝒊𝒔𝒕𝒐𝒓𝒕𝒊𝒐𝒏
𝑺𝒊𝒈𝒏𝒂𝒍
𝑺𝑰𝑵𝑨𝑫 = 𝟐𝟎 × 𝐥𝐨𝐠
𝑵𝒐𝒊𝒔𝒆 + 𝑫𝒊𝒔𝒕𝒐𝒓𝒕𝒊𝒐𝒏
𝑺𝑵𝑹 = 𝟐𝟎 × 𝐥𝐨𝐠
𝑵𝒐𝒊𝒔𝒆𝑹𝑴𝑺
𝝈 = 𝒔𝒕𝒅 𝒅𝒆𝒗
𝑵𝒐𝒊𝒔𝒆𝑷𝑷
𝟒𝝈 𝒕𝒐 𝟔𝝈
4
Typical datasheet noise parameters
Noise parameter
Input-referred noise
SNR
THD
SINAD
Definition
Resolution or internal noise of the ADC (plus programmable gain amplifier [PGA] noise
for integrated devices) specified as a noise voltage source applied to the ADC’s input
pins (before gain).
Ratio of the output signal amplitude to the output noise level, not including harmonics or
DC.
Noise Test
Equation (units)
n/a
𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 (VRMS,VPP)
Input-sinewave (AC)
Indication of a circuit’s linearity in terms of its effect on the harmonic content of a signal,
given as the ratio of the summed harmonics to the RMS signal amplitude.
Input-sinewave (AC)
Ratio of the RMS value of the output signal to the RMS value of all other spectral
components, not including DC.
Input-sinewave (AC)
10 log
10 log
10 log
DC-Input (DC)
log
Noise-free resolution
Dynamic range figure of merit using the ratio of FSR to peak-to-peak noise voltage to
define the maximum number of bits unaffected by peak-to-peak noise.
DC-Input (DC)
log
Figure of merit representing the number of noise-free codes (or counts) that you can
realize with noise.
DC-Input (DC)
Figure of merit relating the SINAD performance to that of an ideal ADC’s resolution with
a certain number of bits (given by the ENOB).
Input-sinewave (AC)
(
)
(
)
(dBc)
∑
(dBc)
(
Dynamic range figure of merit using the ratio of full-scale range (FSR) to RMS noise
voltage to define the noise performance of an ADC.
ENOB
)
)
∑
Effective resolution
Noise-free counts
(
(
(dBc)
(bits)
,
(bits)
,
2(
)
.
.
)
(counts)
(bits)
5
Relative parameters: AC performance example
FFT (ADS127L01)
PARAMETER
FILTER
TYPE
SNR
WB2
VREF
DATA
RATE
TYP
2.5 V
512 kSPS
104.4
3V
512 kSPS
105.8
UNIT
dB
6
Relative parameters: DC performance example
DC Histogram (ADS127L01)
𝐴𝐷𝑆127𝐿01 𝐸𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑅𝑒𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 (𝑉
= 2.5 𝑉, 𝐹𝑆𝑅 = 5 𝑉)
MODE
DATA RATE
(SPS)
VRMS_noise
(µVRMS)
EFFECTIVE
RESOLUTION (Bits)
High-resolution
(HR)
8,000
1.41
21.76
𝐴𝐷𝑆127𝐿01 𝐸𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑅𝑒𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 (𝑉
𝑙𝑜𝑔
𝐹𝑆𝑅
𝑉
,
= 𝑙𝑜𝑔
2𝑉
1.41 ∗ 10
= 1 𝑉, 𝐹𝑆𝑅 = 2 𝑉)
𝑉
= 20.44 𝑏𝑖𝑡𝑠
𝑅𝑒𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 𝐷𝑦𝑛𝑎𝑚𝑖𝑐 𝑅𝑎𝑛𝑔𝑒 𝐿𝑜𝑠𝑠
𝑙𝑜𝑔 % 𝑢𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 = 𝑙𝑜𝑔
𝑵𝒐𝒊𝒔𝒆𝑹𝑴𝑺
𝝈 = 𝒔𝒕𝒅 𝒅𝒆𝒗
2𝑉
= −1.32 𝑏𝑖𝑡𝑠
5𝑉
21.76 − 1.32 = 20.44 𝑏𝑖𝑡𝑠
7
Absolute noise parameters
Noise parameter
Input-referred noise
Definition
Resolution or internal noise of the ADC (plus programmable gain amplifier [PGA] noise
for integrated devices) specified as a noise voltage source applied to the ADC’s input
pins (before gain).
Noise Test
Equation (units)
n/a
𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 (VRMS,VPP)
Noise vs VREF (ADS127L01)
Input-referred noise:
 Is measured directly
 Easily compare
performance for different
ADC
𝑽𝑵𝒐𝒊𝒔𝒆,𝑴𝑨𝑿 = 𝟓. 𝟏𝟕 𝝁𝑽𝑹𝑴𝑺
𝑵𝒐𝒊𝒔𝒆
𝑹𝑴𝑺
𝑽𝑵𝒐𝒊𝒔𝒆,𝑴𝑰𝑵 = 𝟒. 𝟗𝟗 𝝁𝑽𝑹𝑴𝑺
 Simplifies system noise
analysis
8
Lower vs higher resolution (SAR & wideband ΔΣ)
ADS7044 (12-bit)
ADS8920B (16-bit)
ADS8900B (20-bit)
9
Lower vs higher resolution (low-speed ΔΣ)
16-bit ADS114S08 noise – µVRMS (µVPP)
24-bit ADS124S08 noise – µVRMS (µVPP)
76.3 µVRMS for all
data rates
Varies from
0.32 µVRMS to
15 µVRMS
𝑳𝑺𝑩𝑨𝑫𝑺𝟏𝟏𝟒𝑺𝟎𝟖 =
𝟐 ∗ 𝑽𝑹𝑬𝑭 𝟐 ∗ 𝟐. 𝟓
=
= 𝟕𝟔. 𝟑 µ𝑽
𝟐𝑵
𝟐𝟏𝟔
𝑳𝑺𝑩𝑨𝑫𝑺𝟏𝟐𝟒𝑺𝟎𝟖 =
𝟐 ∗ 𝑽𝑹𝑬𝑭 𝟐 ∗ 𝟐. 𝟓
=
= 𝟎. 𝟐𝟗𝟖 µ𝑽
𝟐𝑵
𝟐𝟐𝟒
10
Choosing an ADC
System Designer
Parameter
Specifications
Noise Target
150 nV
Input Range
0 – 10 mV
Sample Rate
50 SPS
System Resolution
16 bits
I’ll choose a 24-bit ADC with 150
nVRMS input-referred noise
11
Thanks for your time!
Please try the quiz.
12
Quiz: ADC noise measurement methods & parameters
1. Which of the following parameters would NOT depend on the maximum
applied input signal amplitude?
a.
b.
c.
d.
e.
SNR.
SINAD.
Effective resolution
Noise Free resolution
Input referred noise.
2. (T/F) Reference voltage typically does not impact the input referend
noise for low resolution ADC.
a. True.
b. False.
13
Quiz: ADC noise measurement methods & parameters
3. (T/F) The table below is representative of a device that is quantization
noise dominated.
a. True
b. False
14
Quiz: ADC noise measurement methods & parameters
4. A dc input signal is applied to an ADC as shown in the figure below. Is
the output histogram indicative of a device that is dominated by thermal
noise or quantization noise.
a. Thermal Noise
b. Quantization Noise
c. You cannot measure noise using the circuit connection shown.
VDD
AINP
VIN
+
_
ADC
AINM
15
Thanks for your time!
16
16
© Copyright 2020 Texas Instruments Incorporated. All rights reserved.
This material is provided strictly “as-is,” for informational purposes only, and without any warranty.
Use of this material is subject to TI’s
, viewable at TI.com
17
Hello, and welcome to the TI Precision Labs module discussing how ADC
manufacturers measure and specify datasheet noise parameters. In a previous
Precision Lab module covering the types of ADC noise, we discussed the two
types of noise intrinsic to ADCs, where this noise comes from, and what it looks
like in both the time and frequency domains. This module will expand upon that
knowledge by covering how ADC noise is measured and characterized, the
various noise parameters found in ADC datasheets, the difference between
relative and absolute noise parameters, and how each parameter type affects
lower- versus higher-resolution ADCs.
Ultimately, this presentation has two goals: first, to be able to help you
understand and interpret ADC datasheet noise plots and specifications; and
second, to motivate you to analyze both ADC and system performance in terms
of input-referred noise whenever possible. In a subsequent presentation, we’ll
take this information and apply it to a design example that compares noise
analysis using different parameters as well as describes how to use this
information to choose an ADC for your application.
To motivate why this discussion is important, let’s attempt to choose an ADC for
some arbitrary application.
1
For example, here’s a typical question we might get from a system designer
when they are ready to choose an ADC: “In my end-equipment, I have
determined our noise target is 150 nV, the input range is 0 to 10 mV, and we
need to sample at 50 SPS. This requires approximately 16 bits of resolution.
Does that mean I can use a 16-bit ADC for my application?”
If you just needed 16 bits of resolution from an ADC, it would be difficult – but
possible – to get this from a 16-bit ADC. However, you absolutely could not use
a 16-bit ADC for this system due to the additional low noise and small input
range requirements, though it’s not always clear why this is the case. One
common system design challenge is correlating your requirements to the
information found in an ADC’s datasheet. To mitigate this challenge, it’s
important to understand how ADC manufacturers measure and specify ADC
noise, as well as know the different ADC noise parameters and their
derivations. This knowledge will equip you to confidently read an ADC’s
datasheet, compare this information to your system’s specifications and confirm
it is the right device for your design.
2
There are two methods ADC manufacturers use to measure ADC noise. The
first method involves applying a sinewave with a specific amplitude and
frequency to report how the ADC quantizes this signal. We use this method
primarily for SAR and wideband delta-sigma ADCs where AC performance is
critical. Both single-ended and differential input configurations are shown on the
left. Also, the test input signals can be referenced to some voltage other than
ground, such as mid-supply, as long as the ADC can support this configuration.
The second noise measurement test involves applying a DC input to the ADC
to measure intrinsic ADC noise. Lower-resolution SAR ADCs typically use the
single-ended DC-input method, as higher-resolution SAR and most delta-sigma
ADCs offer differential inputs. This specific measurement method employs a
small DC offset or a mid-supply voltage as a test input, and does not have to be
ground-referenced if the ADC can tolerate pseudo-differential signals. The
measured results include some reference noise due to the nonzero input,
though usually the ADC’s noise is comparatively larger. You can watch the
Precision Labs video on the effects of reference noise on ADC performance to
learn more about this topic.
For differential ADCs, the device’s inputs are typically shorted together to midsupply. With a zero volt input, no reference noise is present in the
measurement. Therefore, the input-short variation of the DC-input test provides
the purest measurement of an ADC’s intrinsic noise. Higher-resolution ADCs
primarily use this method since low noise performance is critical.
3
Let take a look at an ADC that includes both sinewave-input and DC-input
measurements to better understand how noise is represented in an ADC’s datasheet.
3
The plots shown here graphically represent the noise measured using the
methods on the previous slide. These specific plots are from the ADS127L01’s
data sheet, a 24-bit, 512 kSPS, wideband delta-sigma ADC with a flat
passband digital filter. Therefore, we use the sinewave-input test to
characterize this ADC’s noise and report it graphically using the FFT on the left.
We report the amplitude of three characteristics from the FFT: the signal,
highlighted in green; the noise, highlighted in red; and distortion, highlighted in
purple. Using these three characteristics, we are able to determine common
noise metrics including signal to noise ratio, or SNR; total harmonic distortion,
or THD; and SINAD, or signal to noise plus distortion ratio.
The ADS127L01’s low noise allows it to be used in low-speed, DC precision
applications as well. As a result, this ADC’s datasheet also includes a histogram
of the output noise with shorted inputs, shown on the right. With a 0 V DC
signal applied to the input, the histogram characterizes the ADC’s intrinsic
noise. As was discussed in the previous module introducing types of ADC
noise, the ADS127L01’s high resolution yields a very small LSB size such that
thermal noise dominates. Thermal noise’s broadband frequency spectrum
results in the Gaussian output code distribution shown here. This dataset’s
standard deviation is the ADC’s RMS noise, represented by the blue lines on
the histogram. One standard deviation represents 68% of the dataset, and is
effectively the typical noise value you can expect. Comparatively, four to six
standard deviations – the exact multiplier depends on an ADC’s noise
distribution – represents the peak to peak voltage noise. Six standard
deviations represents 99.7% of the dataset, and is effectively a maximum noise
value. While it is true that peak-to-peak noise can fall outside these limits, it is
4
statistically unlikely and impractical to consider. You can learn more about why this is
true in the Precision Labs module on the statistics behind error analysis.
Now that you can identify and interpret noise plots in an ADC’s datasheet, let’s
consider how we use this information to calculate ADC noise parameters
4
Shown here are the most common noise parameters found in an ADC’s datasheet, as well as their definitions and equations.
While this table contains a lot of information, there are a few key takeaways you should consider.
First, noise parameters generally describe either AC or DC performance. We introduced SNR, THD and SINAD on the previous
slide, as well as discussed how these parameters are derived from an FFT as a result of the input-sinewave test. One important
point captured in this table is that these equations always use the RMS signal and noise, not peak-to-peak. Therefore you must
make sure to convert your signal and noise values if they are not already in this form. Also, ENOB, or effective number of bits, is
another AC performance specification you might see in an ADC’s datasheet, and is derived from SINAD. ENOB is a figure of
merit defined as an ADC’s actual resolution under a given set of conditions, as opposed to its ideal resolution. Comparatively,
effective resolution, noise-free resolution and noise-free counts represent different ways of reporting DC noise performance. We
derive these noise parameters from a histogram via the DC-input test. You can see that effective resolution is relative to the
ADC’s RMS noise, while noise-free resolution is relative to an ADC’s peak to peak noise. In that context, effective resolution is
similar to an ADC’s typical dynamic range, while noise-free resolution describes an ADC’s maximum dynamic range under the
given conditions. In both cases, the full-scale range is a raw value, not RMS or peak-to-peak as it is with the AC parameters.
Also worth noting here is that many engineers use the terms “ENOB” and “effective resolution” synonymously to describe an
ADC’s DC performance. However, unlike effective resolution, ENOB is purely a dynamic performance specification derived from
SINAD and is not meant to convey DC performance. These terms will be used accordingly throughout the rest of this
presentation.
Finally, an important characteristic of all of the calculated parameters highlighted here is that they involve some ratio of values.
These are referred to as “relative parameters”. As the name implies, these parameters provide a noise performance metric
relative to some absolute value, usually the input signal or the full-scale range. The next few slides will examine this concept in
more detail.
5
To understand what a relative parameter is and why it matters, let’s revisit the ADS127L01’s FFT shown earlier in this
presentation. We had discussed the importance of the input signal’s frequency and amplitude in general, but we didn’t look at
the actual values of these specifications in detail. In particular, this plot uses an input signal at -0.5 dB relative to full scale,
where full scale is 2.5 V.
If you look at the SNR values in the ADS127L01’s datasheet shown here on the right side of the screen, you’ll find that using this
input signal and these ADC settings, you can expect a typical SNR of 104.4 dB. However, if you instead choose a different fullscale voltage or input signal amplitude, you won’t be able to achieve datasheet performance even if all other input conditions are
identical.
As an example, notice that the table also includes SNR values characterized at a 3 volt reference voltage. Compared to the 2.5
volt reference voltage, you can expect a small increase in SNR even when all other conditions are equal. For our specific case,
the 0.5 V increase in reference voltage boosts SNR performance to 105.8 dB, or an increase of 1.4 dB.
You can see this effect with DC noise parameters as well
6
Shown here is the ADS127L01’s noise histogram as well as part of its effective resolution table. Recall that effective resolution
is calculated used the RMS noise and is relative to the ADC’s full scale range – this equation is restated here for reference.
Therefore, similar to the SNR example we just looked at, using a different reference voltage changes the device’s achievable
dynamic range, since full scale range depends on the ADC’s reference voltage. However, unlike the SNR example that used a
nonzero input, the differential DC-input test used to characterize the ADS127L01 employed a shorted-input, zero-volt signal.
Since this implies zero bits of effective resolution, most ADC manufacturers instead specify effective resolution assuming the full
scale range is maximized. This choice yields the largest possible ADC dynamic range at those settings, but requires the input
signal to equal the full-scale range. If this is not the case for your system, you won’t be able to achieve the effective resolution
values specified in the data sheet.
As an example, if you were operating the ADS127L01 at the conditions in the histogram using a 2.5 V reference, you could
expect 1.41 µV of input-referred RMS noise and 21.76 bits of effective resolution. But, if your application instead used a 1 V
reference voltage, this reduces the full-scale range to 2 V since the ADS127L01’s full-scale range is equal to ±VREF. Reducing
the full-scale range reduces the ADC’s effective resolution to 20.44 bits at these settings, assuming the RMS noise remains
unchanged. We will confirm this assumption on the next slide.
You can generalize this effective resolution loss using the formula shown here, where percent utilization is the ratio of the
application-specific full-scale range to the full-scale range at which the ADC’s noise is characterized. In this case, changing the
full-scale range reduced the ADS127L01’s effective resolution by more than 1.3 bits. Ultimately, SNR and effective resolution,
like most of the noise parameters we’ve identified so far, are relative. They will always have to be defined in relationship to some
absolute value such as reference voltage, making it challenging to compare specs among different ADCs. This might cause you
to wonder: is there such a thing as an absolute noise parameter?
7
You may have noticed that we introduced eight different metrics in the noise parameter table but have only discussed the seven
calculated parameters so far. The remaining metric, input-referred noise, is the only one that is measured directly. As such, it is
the only absolute noise parameter on the list, and the one we recommend you use to perform ADC noise analysis whenever
possible. Using an absolute parameter allows you to quickly and fairly compare different ADCs across different manufacturers.
Absolute parameters also simplify system noise analysis, since datasheet input-referred noise levels are often independent of
input signal or reference voltage.
While we claim this is true, you may recall that the previous Precision Labs module covering ADC noise types stated that
changing an ADC’s reference voltage can affect its overall noise since it changes the LSB size. This is true for ADCs where
quantization noise dominates. For thermal-noise dominant ADCs, reference voltage has little impact on noise performance. To
prove this point, shown here is the input-referred noise versus reference voltage plot from the ADS127L01’s datasheet, the ADC
we’ve been analyzing throughout this presentation. The reference voltage extends from 0.5 V to 3 V, representing the minimum
and maximum values possible for this device. The maximum change in input-referred noise over this span is 0.18 µV RMS.
Relative to the change in reference voltage, which is 2.5 V, this variation equates to only 0.072 ppm. In other words, the
ADS127L01’s noise experiences no significant change with respect to reference voltage, so you can consider this ADC’s inputreferred noise absolute under the given conditions.
The crossover point between lower and higher resolution ADCs generally occurs at the 16-bit level. For ADCs with 16-bit or less
resolution, reference voltage affects ADC noise levels and should not be considered a true absolute parameter. On the other
hand, thermal noise dominates in ADCs with greater than 16 bits of resolution, so changes in reference voltage do not affect
noise performance as shown here using the ADS127L01. Unfortunately, most ADCs do not have a noise versus reference
voltage plot to verify this claim. Instead, you can look at the more common noise plots discussed throughout this module to
confirm this delineation
8
Shown here are the histograms for three different SAR ADCs, ranging from 12
to 20 bits. Though we will use SAR ADCs for this example, this same analysis
applies to any ADC with a histogram, including wideband delta-sigmas.
On the left, almost all of the 12-bit ADS7044’s output codes fall into one bin.
Minimal code spread indicates a quantization-noise dominant ADC, since the
random nature of thermal noise would result in a Gaussian code distribution.
Instead, this lower-resolution ADC’s thermal noise is obscured by quantization
noise. Since quantization noise is directly related to LSB size and reference
voltage, lower-resolution ADC input-referred noise is relative. Fortunately, noise
is not typically an important system design challenge when using lowerresolution ADCs, especially compared to solution size, power consumption and
cost.
On the right, the 20-bit ADS8900B histogram shows significant code spread
and a 2 ppm or 2.1 LSB standard deviation. This indicates a thermal noise
dominant ADC, since the RMS noise is greater than the LSB size. The
histogram’s Gaussian code distribution further indicates the random, broadband
nature of thermal noise compared to quantization noise. Therefore, you can
conclude that the ADS8900B’s input-referred noise is absolute. Since noise
performance at the 20-bit level is critical, using an absolute parameter provides
a standard value to compare against other potential noise sources, as well as
enables you to easily compare this value against other 20-bit ADCs to
determine which one meets all of your system requirements.
9
Finally, at the 16-bit level, you can see elements of both quantization noise and
thermal noise. Some code spread exists that might indicate thermal noise, but the
standard deviation is still less than ±0.5 LSB. You can also see that one bin clearly
dominates, but the absolute frequency of hits in this bucket – around 1,350 – is
considerably smaller than the 12-bit ADC’s frequency, at around 65,000 hits. Taken
together, these observations demonstrate that the transition point between a
quantization noise and thermal noise dominant ADC generally occurs at the 16-bit
level, as we claimed on the previous slide.
For those ADC that do not include histograms in their datasheets – typically lowspeed delta-sigmas – you can identify a quantization noise versus thermal noise
dominant ADC using a noise table
9
Shown here are the datasheet noise tables for the 16-bit ADS114S08 and the
24-bit ADS124S08, two low-speed delta-sigma ADCs. These devices come
from the same product family and other than their resolutions are identical. This enables a fair noise
performance comparison, shown here in microvolts RMS and peak to peak using a 2.5 V reference voltage. Note that
the 16-bit ADC input-referred noise is the same for all data rates, at 76.3 µVRMS.
On the other hand, the 24-bit ADC input-referred noise values vary depending
on the data rate. Even though both delta-sigma ADCs benefit from
oversampling and noise shaping at the different data rates, the lower-resolution
ADC exhibits constant noise at any speed, while the higher-resolution ADC
noise changes. Why does this occur? Calculating each ADC’s respective LSB
size helps clarify how this relates to quantization versus thermal noise dominant
ADCs.
For the 16-bit ADC, the ADC noise equals the LSB size, due to the fact that
quantization noise dominates in lower resolution ADCs. For this ADC, inputreferred noise is relative. This allows you to reduce the overall noise by
decreasing the reference voltage. For example, a 1.25 V reference would
reduce the ADS114S08’s noise to 38.15 µVRMS at any data rate and gain equal
to one. On the other hand, the 24-bit ADC’s quantization noise is 0.298 µV
RMS, lower than any value shown in the table on the right. In this case, thermal
noise obscures the very low levels of quantization noise. Reducing quantization
noise for the 24-bit ADC would have no effect on the overall ADC noise
performance, so the ADS124S08’s input-referred noise is considered absolute.
This reiterates the claim that the transition point between a quantization noise
and thermal noise dominant ADC generally occurs at the 16-bit level. Moreover,
like the 20-bit ADS8900B on the previous slide, the absolute nature of the
10
ADS124S08’s input-referred noise provides a standard value to compare against
other potential noise sources, as well as enables you to easily compare this value
against other 24-bit ADCs to determine if they meet all of your system requirements.
To that end, let’s revisit the hypothetical request made at the beginning of this
presentation
10
Even with the same system requirements, it’s easier to choose an ADC based
on the input-referred noise requirements since there is no direct correlation
between your system’s resolution and an ADC’s resolution. Does that mean
using a 24-bit ADC to get 16-bit resolution forces you to pay for performance
that the device cannot actually provide? No, because 16-bit resolution doesn’t
necessarily tell you anything about how much of the full-scale range you use, or
how finely those bits needs to be resolved. You may only need 16 bits of
resolution, but if the minimum input signal is 150 nV you’ll never be able to
resolve that with a 16-bit ADC. The true benefit of a high-resolution ADC is the
low levels of input-referred noise it offers. It does not mean that effective
resolution is unimportant – just that it is not the best way to parameterize a
system.
Now that you better understand how noise is represented in an ADC’s
datasheet and why input-referred noise is the best parameter for noise
analysis, the next Precision Labs module applies this information to a practical
example so you can choose an ADC for your next design.
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That concludes this video. Thank you for watching. Please try the quiz to check
your understanding of this video's content.
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Which of the following parameters would NOT depend on the maximum applied
input signal amplitude?
The correct answer is “e”, input referred noise. All the other parameters are
relative in that they are impacted by the signal amplitude or full scale range.
(T/F) Reference voltage typically does not impact the input referend noise for
low resolution ADC.
The correct answer is “b” false. For low resolution ADC they are often
quantization noise dominant. In this case the ADC reference voltage will impact
the input referred noise. On the other hand, the high resolution devices are
usually dominated by thermal noise so they are not impacted by the reference
voltage.
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(T/F) The table below is representative of a device that is quantization noise
dominated.
The correct answer is “b” false. If the device was quantization noise limited the
data rate would not impact the total noise and it would be the same for every
data rate.
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A dc input signal is applied to an ADC as shown in the figure below. Is the
output histogram indicative of a device that is dominated by thermal noise or
quantization noise.
The correct answer is “b” quantization noise. If this were thermal noise
dominate you would expect to see a Gaussian distribution. In this case the
noise is mostly confined to one code, so the noise is mostly quantization noise.
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