Linearization of analog-to-digital converters

Dent, A.C.; Cowan, C.F.N.

doi:10.1109/31.55031

Cited by 17 publications

(5 citation statements)

References 7 publications

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“…Studied cases included the usage of analog to digital converters to solve nonlinearities at the same time that the conversion is made [3-4]. Also, type R-2R digital to analog converters have proven the necessity to improve the linear response [5]. Other research focused on improving the nonlinear response of specific sensors, like the thermistor [6] and the Hall Effect current sensor [7].…”

Section: Introductionmentioning

confidence: 99%

Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors

Rivera

Herrera

Chacon³

et al. 2008

Sensors

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The development of intelligent sensors involves the design of reconfigurable systems capable of working with different input sensors signals. Reconfigurable systems should expend the least possible amount of time readjusting. A self-adjustment algorithm for intelligent sensors should be able to fix major problems such as offset, variation of gain and lack of linearity with good accuracy. This paper shows the performance of a progressive polynomial algorithm utilizing different grades of relative nonlinearity of an output sensor signal. It also presents an improvement to this algorithm which obtains an optimal response with minimum nonlinearity error, based on the number and selection sequence of the readjust points. In order to verify the potential of this proposed criterion, a temperature measurement system was designed. The system is based on a thermistor which presents one of the worst nonlinearity behaviors. The application of the proposed improved method in this system showed that an adequate sequence of the adjustment points yields to the minimum nonlinearity error. In realistic applications, by knowing the grade of relative nonlinearity of a sensor, the number of readjustment points can be determined using the proposed method in order to obtain the desired nonlinearity error. This will impact on readjustment methodologies and their associated factors like time and cost.

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Section: Introductionmentioning

confidence: 99%

Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors

Rivera

Herrera

Chacon³

et al. 2008

Sensors

View full text Add to dashboard Cite

show abstract

“…Cases studied included the use of analog to digital converters to solve nonlinearities at the same time that the conversion is made [ 2 - 3 ]. In digital to analog type R-2R converters have also proved to be necessary to improve the linear response [ 4 ]. Other work has focused on improving the nonlinear response of specific sensors, like the thermistor [ 5 ] and the Hall effect current sensors [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks

Rivera¹,

Carrillo²,

Chacon³

et al. 2007

Sensors

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Abstract:The development of smart sensors involves the design of reconfigurable systems capable of working with different input sensors. Reconfigurable systems ideally should spend the least possible amount of time in their calibration. An autocalibration algorithm for intelligent sensors should be able to fix major problems such as offset, variation of gain and lack of linearity, as accurately as possible. This paper describes a new autocalibration methodology for nonlinear intelligent sensors based on artificial neural networks, ANN. The methodology involves analysis of several network topologies and training algorithms. The proposed method was compared against the piecewise and polynomial linearization methods. Method comparison was achieved using different number of calibration points, and several nonlinear levels of the input signal. This paper also shows that the proposed method turned out to have a better overall accuracy than the other two methods. Besides, experimentation results and analysis of the complete study, the paper describes the implementation of the ANN in a microcontroller unit, MCU. In order to illustrate the method capability to build autocalibration and reconfigurable systems, a temperature measurement system was designed and tested. The proposed method is an improvement Sensors 2007, 7 1510 over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.

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“…With proper digital correction (involving some sort of measurement of each chip), it is thus possible to reduce both the area and the power consumption of the analog circuits. ADCs designed (more or less) in this spirit have been reported in [4]- [8]. All of these papers consider flash ADCs with low-precision comparators.…”

Section: Introductionmentioning

confidence: 99%

On the Static Resolution of Digitally Corrected Analog-to-Digital and Digital-to-Analog Converters With Low-Precision Components

Frey

Loeliger

2007

IEEE Trans. Circuits Syst. I

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Abstract-This semi-tutorial paper considers the effect of component mismatch on the static accuracy of analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) with digital correction. First, it is noted that the effective static resolution of flash ADCs is not much reduced by component mismatch: with proper digital correction, the loss due to mismatch is only about 1.3 bit, virtually independently of the mismatch level unless the mismatch is very small. Second, it is noted that current steering DACs may actually benefit from component mismatch. Moreover, with proper digital correction, current steering DACs can achieve an effective static resolution of bits with as few as +2 near-unit low-precision current sources.Index Terms-Analog-to-digital converter (ADC), calibration, digital-to-analog converter (DAC).

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Linearization of analog-to-digital converters

Cited by 17 publications

References 7 publications

Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors

Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors

Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks

On the Static Resolution of Digitally Corrected Analog-to-Digital and Digital-to-Analog Converters With Low-Precision Components

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