A state of the art on ADC modelling

Arpaïa, Pasquale; Daponte, P.; Rapuano, Sergio

doi:10.1016/s0920-5489(03)00060-6

Cited by 37 publications

(20 citation statements)

References 49 publications

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“…The next transformation of voltage sources with constant internal resistance into current sources leads to the final structural DAC error model as shown in Fig.7. [3], [10], [26]. Internal resistance connected to the virtual zero of operational amplifier (OA) does not contribute to the error behaviors.…”

Section: The Successive Approximation Adcmentioning

confidence: 99%

Error Models of the Analog to Digital Converters

Michaeli¹,

Šaliga

2014

Measurement Science Review

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Error models of the Analog to Digital Converters describe metrological properties of the signal conversion from analog to digital domain in a concise form using few dominant error parameters. Knowledge of the error models allows the end user to provide fast testing in the crucial points of the full input signal range and to use identified error models for post correction in the digital domain. The imperfections of the internal ADC structure determine the error characteristics represented by the nonlinearities as a function of the output code. Progress in the microelectronics and missing information about circuital details together with the lack of knowledge about interfering effects caused by ADC installation prefers another modeling approach based on the input-output behavioral characterization by the input-output error box. Internal links in the ADC structure cause that the input-output error function could be described in a concise form by suitable function. Modeled functional parameters allow determining the integral error parameters of ADC. Paper is a survey of error models starting from the structural models for the most common architectures and their linkage with the behavioral models represented by the simple look up table or the functional description of nonlinear errors for the output codes.

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Section: The Successive Approximation Adcmentioning

confidence: 99%

Error Models of the Analog to Digital Converters

Michaeli¹,

Šaliga

2014

Measurement Science Review

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“…Although mainly developed for physical runs, their use in simulation presents several improvement opportunities difficult or impossible in physical experiments [6]. In particular, Response Surface-modeling [8]- [9] allows statistical a-posteriori behavioral modeling [1]. More recent Multiple Response Surface (MRS) techniques provide better estimation of the local function nonuniformity (though biased) [9].…”

Section: Experiments Design Techniques Optimizes Test Burden In Model mentioning

confidence: 99%

“…the overall acquisition amplitude random noise (modeled as additive [1] and expressed as its standard deviation V σ ), the time stamp jitter noise (in terms of its standard deviation T σ ), and the deterministic nonlinearity of the input/output characteristic. At this last aim, Kim proposed a meaningful model [17], relating the characteristic shape to the FFT test results: in particular, a parameter χ takes into account the asymmetry of the characteristic, while a parameter ξ its exponential nonlinearity.…”

Section: B Metamodel Definitionmentioning

confidence: 99%

“…In digital instrument design, metrological analysis by model simulation plays a key role during project assessment and enhancement [1]. Several indexes of metrological performance have to be evaluated in frequency and time domains (e.g., signal-to-noise-ratio, total harmonic distortion, integral and differential nonlinearity).…”

Section: Introductionmentioning

confidence: 99%

“…This has to be carried out systematically, at varying operating conditions, influence parameters, and uncertainty sources, as well as, moreover, over the input range as a whole. During the conceptual design, behavioral modeling is useful for the sake of the generalization [1]: the digitizing chain is characterized by input-output analytical or numeric relations, without going in deep into the physical structure. In this way, a general perspective is kept, useful for several applications, owing to its independence on the specific architecture of the instrument and on its physical realization.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Surface-Response-Based Modeling of Digitizers: A Case Study on a Fast Digital Integrator at CERN

Arpaïa

Inglese

Spiezia

et al. 2009

IEEE Trans. Instrum. Meas.

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A statistical approach to behavioral modeling for assessing dynamic metrological performance during the concept design of accurate digitizers is proposed. A surface-response approach based on statistical experiment design is exploited for avoiding unrealistic hypothesis of linearity, optimizing simulation, exploring operating conditions systematically, as well as verifying identification and validation uncertainty. An actual case study on the dynamic metrological characterization of a Fast Digital Integrator for high-performance magnetic measurements at the European Organization for Nuclear Research (CERN) is presented.

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AD and DA conversion

Björsell

2015

Modern Measurements

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A state of the art on ADC modelling

Cited by 37 publications

References 49 publications

Error Models of the Analog to Digital Converters

Error Models of the Analog to Digital Converters

Surface-Response-Based Modeling of Digitizers: A Case Study on a Fast Digital Integrator at CERN

AD and DA conversion

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