An analysis methodology to identify dominant noise sources in D/A and A/D converters

Connelly, J.A.; Taylor, Ken

doi:10.1109/31.97533

Cited by 20 publications

(3 citation statements)

References 7 publications

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“…This generates both a static error as well as harmonic and intermodulation distortion 43 . The main sources of thermal and semiconductor noise are the resistor network producing the output voltage levels, the voltage reference, and the output buffer [43][44][45] . Additional non-linearity is introduced by slew-rate limitations in the output stage and glitches caused by non-ideal transistor switching 43,46,47 .…”

Section: Noise and Distortion In Dacsmentioning

confidence: 99%

Existing methods for improving the accuracy of digital-to-analog converters

Eielsen

Fleming

2017

Review of Scientific Instruments

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The performance of digital-to-analog converters is principally limited by errors in the output voltage levels. Such errors are known as element mismatch and are quantified by the integral non-linearity. Element mismatch limits the achievable accuracy and resolution in high-precision applications as it causes gain and offset errors, as well as harmonic distortion. In this article, five existing methods for mitigating the effects of element mismatch are compared: physical level calibration, dynamic element matching, noise-shaping with digital calibration, large periodic high-frequency dithering, and large stochastic high-pass dithering. These methods are suitable for improving accuracy when using digital-to-analog converters that use multiple discrete output levels to reconstruct time-varying signals. The methods improve linearity and therefore reduce harmonic distortion and can be retrofitted to existing systems with minor hardware variations. The performance of each method is compared theoretically and confirmed by simulations and experiments. Experimental results demonstrate that three of the five methods provide significant improvements in the resolution and accuracy when applied to a general-purpose digital-to-analog converter. As such, these methods can directly improve performance in a wide range of applications including nanopositioning, metrology, and optics.

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Section: Noise and Distortion In Dacsmentioning

confidence: 99%

Existing methods for improving the accuracy of digital-to-analog converters

Eielsen

Fleming

2017

Review of Scientific Instruments

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“…where E n is the noise voltage of the DAC in V/ ͌ Hz, and E ref and E A are the noise voltage of the reference-voltage source and the voltage-follower amplifier in the DAC, respectively [5]. R is the equivalent resistance of the R-2R resistor network in the DAC, k is the Boltzmann's constant, and T is absolute temperature in Kelvin.…”

Section: System Analysis and Designmentioning

confidence: 99%

“…The photosensitivity of a variety of fibers has been investigated and compared (for a review, see [5]), including germanosilica fibers with standard or increased germanium concentration and fibers with dopands such as boron or deuterium. Usually, the photosensitivity of these special fibers is enhanced by the incorporation of extra Ge doping, which varies from 15% mol to 25% mol, depending on the manufacturer.…”

Section: Introductionmentioning

confidence: 99%

A low-voltage fast-switching frequency synthesizer at 2.4 GHz

Tsang

Yuen

2004

Microw. Opt. Technol. Lett.

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is found to be 0.06 and 0.074, respectively, whereas the presented method shows an average error of 0.032 for length and 0.018 for width. Thus, an ANN-coupled GA gives better results, as compared to the formulas derived in [4]. CONCLUSIONA novel method of coupling an ANN with a GA in order to calculate the dimensions of a thick-substrate microstrip antenna has been discussed in this paper. The measure of accuracy of the solution obtained by the GA depends directly upon the efficient training of the neural networks. Thus, care must be taken for an efficient training of the network. In cases where there is no accurate theoretical formulation for the objective function, this technique can be used for optimization purposes.Simultaneous optimization of the dielectric constant, height of the substrate, dimensions, and so forth is possible in the present method, whereas in the conventional method, it is either computationally complex or not possible. The results obtained by the ANN-coupled GA are compared with the experimental results. The results are in very good agreement with the experimental findings. In the presented method, the simulation time is less than the simulation times of methods such as the method of moments (MoM), finite-difference time-domain (FDTD), and finite-element technique (FET), without compromising the error. The accuracy of the proposed model can be increased by using a more effective ANN algorithm. Furthermore, the accuracy can be increased by taking more experimental results for training the ANN. This method may contribute to the improvement of ANN-based techniques for solving problems such as array-factor correction, cross-polarization reduction, bandwidth enhancement, array optimization, and so on.

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Computer generation of colored noise for time-domain analysis of integrated circuits

Casinovi

1996

Analog Integr Circ Sig Process

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An analysis methodology to identify dominant noise sources in D/A and A/D converters

Cited by 20 publications

References 7 publications

Existing methods for improving the accuracy of digital-to-analog converters

Existing methods for improving the accuracy of digital-to-analog converters

A low-voltage fast-switching frequency synthesizer at 2.4 GHz

Computer generation of colored noise for time-domain analysis of integrated circuits

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