Signal Chain Basics #87: ADC DNL in Precision Signal Chain Error Analysis

For data acquisition systems that require the highest precision dynamic performance, integral nonlinearity (INL) can become a contributing error factor when offset, gain, and noise are removed through calibration and averaging. But what effect does the differential nonlinearity (DNL) of the analog-to-digital converter (ADC) have on the overall system errors?

To start, INL can be degraded by non-idealities introduced by limitations in common-mode rejection ratio (CMRR), slew rate, and settling from the signal chain preceding an ADC. Unlike INL, DNL is only affected by the internal design of the ADC. DNL is the result of internal capacitor mismatch, dielectric absorption and leakage, settling, as well as internal reference settling, and comparator performance.

Figure 1: INL Error in ADC transfer function due to long DNL width.

Figure 1: INL Error in ADC transfer function due to long DNL width.

DNL is defined as the difference between two adjacent codes minus 1 least significant bit (LSB). Therefore, if the ADC transfer function is perfect, the step difference between codes is 1 LSB. This results in a DNL error = 0. Mathematically, if S(i) is the ADC transfer function at a single code (i) and VLSB is the ADC’s LSB, DNL can be defined as the discrete derivative of the ADC transfer curve normalized to 1 LSB (equation 1):

In an endpoint calibrated ADC transfer function, the width of each individual code may vary. Figure 1 illustrates these differences. However, because offset and gain are assumed to be calibrated, there is no non-linearity at the end-points of the ADC transfer function. Hence, the sum of the total DNL widths remain constant at the positive full scale code (equation 2):

Figure 1 also illustrates that DNL can directly affect the INL error because a large code width can shift the transition point further off the gain curve But does this necessarily imply that a large code needs to be immediately followed by a successive small code? Not necessarily. Instead of a single negative code, the ADC can accumulate negative DNL on several codes against positive DNL on a single code. Therefore, it is possible for an ADC to have no missing codes and still have an asymmetric DNL specification (for example: –0.99 to 1.5 LSB).

Figure 2: DC transfer function showing transition (DC) noise riding on DNL steps.

Figure 2: DC transfer function showing transition (DC) noise riding on DNL steps.

In most cases the INL and DNL maximum are better than the uncertainty added by the typical DC (transition) noise specification (Figure 2). If the typical DC noise is specified at 0.7 LSB (RMS) as an example, the peak-to-peak noise will be somewhere between 4 and 5 LSBs. This is almost three times the maximum DNL specification. This noise can be reduced by square-root of n through averaging, where n is the number of averages taken for a given sample.

For both static and dynamic precision measurements, the ultimate goal is to optimize the effective number of bits (ENOB) for a given input. DNL affects the INL which can cause THD degradation. This can result in harmonic tones created by passing a sine wave through a nonlinear transfer function. DNL also causes abrupt transitions in the transfer function in the time domain. This results in higher order harmonics that can affect the signal-to-noise and distortion (SINAD), which directly impacts the ENOB in Equation 3.

In summary, the ADC DNL can affect the monotonicity and errors in a DC transfer function. It can play a role in degrading dynamic specifications such as signal-to-noise ratio (SNR), THD, and SINAD in a precision data acquisition system. Ultimately this can result in lower ENOB and a decrease in overall system precision.

Please join us next time when we will address fully differential amplifier basics and single end to differential applications.

— Matthew Hann, Texas Instruments


  • Moscovici, Alfi. High Speed A/D Converters. Kluwer Academic Publishers. 2001.
  • Pallet, Dominique and Machado da Silva. Dynamic Characterisation of Analogue-to-Digital Converters. Kluwer Academic Publishers, Dordrecht, The Netherlands.

6 comments on “Signal Chain Basics #87: ADC DNL in Precision Signal Chain Error Analysis

  1. samicksha
    March 28, 2014

    I guess it is not even possible to remove its effects with calibration.

  2. Sachin
    March 31, 2014

    This is a very informative piece with interesting insights. Differential nonlinearity, unlike physical factors such as noise, is very difficult to eliminate and can contribute errors to the overall precision of the measurements. This piece reveals some interesting yet simple ways to not only deal with DNL in the analog-digital-converter but also how to compensate for Integral non-linearity.

  3. Sachin
    March 31, 2014

    Errors in a direct current transfer function can be affectedby ADC DNL, what I wonder is how it play a role in degrading dynamic specifications in a precision data acquisition system. Though, DNL affects the INL which intern causes THD degradation, but still it's not clear how. May be it has little effects on signal to noise ratio.

  4. SunitaT
    March 31, 2014

    After reading this piece I can hardly wait for the next installment on the basics of the differential amplifier. In particular, the bit on the single, end-to-end differential applications should make for some very interesting reading. Am not sure exactly how this will be affected by both differential non linearity as well as integral non linearity so I hope that the next installment in this series will shed more light on this subject.

  5. pankajpc
    April 3, 2014

    Internal refernece should not matter if it settles or not when it comes to pipeline adc. The reason was this is that the same refernce is used in all pipeline adc stages. 

  6. yalanand
    April 30, 2014

    This item is quite informative as it gives in the details well and in full. It is quite an educative piece. It gives a general scope on the signal chain basics.. it helps one understand what they should know about data acquisition. It breaks down the codes well to enable one to understand the formula that has been used to come up with the equation. The fact that it uses both static and dynamic precision is what will enable it to be used by many clients in the market.

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