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A study of capacitor array calibration for a successive approximation analog-to-digital converter

Analog-to-digital converters (ADCs) are driven by rapid development of mobile communication systems to have higher speed, higher resolution and lower power consumption. Among multiple ADC architectures, successive approximation (SAR) ADCs attract great attention in mixed-signal design community recently. It is due to the fact that they do not contain amplification components and the digital logics are scaling friendly. Therefore, it is easier to design a SAR ADC with smaller component size in advanced technology than other ADC architectures, which decreases the power consumption and increases the speed of the circuit. However, capacitor mismatch limits the minimum size of unit capacitors which could be used for a SAR ADC with more than 10 bit resolution. Large capacitor both limits conversion speed and increases switching power. In this design project, a novel switching scheme and a novel calibration method are adopted to overcome the capacitor mismatch constraint. The switching scheme uses monotonic switching in a SAR ADC to gain one extra bit, and switches a dummy capacitor between the common mode voltage level (Vcm) and the ground (gnd) to obtain another extra bit. To keep the resolution constant, the capacitor number is reduced by two. The calibration method extracts missing code width to estimate the actual value of capacitors. The missing code extraction is accomplished by detecting metastable state of a comparator, forcing the current bit value and using less significant bits to measure the actual capacitor value. Dither method is adopted to improve calibration accuracy. Behavior model simulation is provided to verify the effectiveness of the calibration method. A circuit design of a 12 bit ADC and the simulation for schematic design is presented in this report. / text

Identiferoai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/26338
Date07 October 2014
CreatorsMa, Ji, active 2013
Source SetsUniversity of Texas
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf

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