Brownian-Bridge-Based Statistical Analysis of the DAC INL Caused by Current Mismatch

Radulov, Georgi; Heydenreich, Markus; Hofstad, R.W. van der; Hegt, J.A.; Roermund, Arthur van

doi:10.1109/tcsii.2006.886900

Cited by 19 publications

(10 citation statements)

References 8 publications

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“…This is a problem, especially because we want to do the reverse: from INL max go to the required I σ and then define the layout. Therefore, we investigated this relation analytically, via Brownian Bridge theory [18]. See Figure 8.…”

Section: Csmentioning

confidence: 99%

Smart, flexible, and future-proof data converters

Roermund

2007

2007 18th European Conference on Circuit Theory and Design

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This paper shows a vision on the future of AD and DA converters. Digitization of systems shifts the physics-related problems to the analog front-end, which is becoming a major bottleneck. Instead of the conventional system-function oriented structure we foresee the front-ends to become dictated by the conversion function including all associated signal conditioning. This conversion will take place in a close and concerted cooperation of analog and digital sub functions. Boundaries between signal conditioning and converter sub functions and between analog and digital will fade. It is further argued that current trends show a lot of conflicts for the future, and that a vision-based approach and new concepts are required to find a way out. The smart converter concept is proposed as a way to overcome the problems and to provide flexible and future proof converters. The way to smart converters is illustrated with research activities in which the author is involved.

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Section: Csmentioning

confidence: 99%

Smart, flexible, and future-proof data converters

Roermund

2007

2007 18th European Conference on Circuit Theory and Design

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“…The use of INL as a yield metric for data converters is prevalent in literature, but has limited utility in system design. Although the bulk of the analytic work has focused on developing INL yield models for current-steering DACs [9]- [13] in the presence of transistor drain current mismatch [14], the major results of these works are also generally applicable to ADCs. According to [13], the analytical development of INL as a yield metric begins with [9], where the maximum deviation of the INL is introduced as a measure for distinguishing between good and bad current-steering DACs.…”

Section: Introductionmentioning

confidence: 99%

“…Although the bulk of the analytic work has focused on developing INL yield models for current-steering DACs [9]- [13] in the presence of transistor drain current mismatch [14], the major results of these works are also generally applicable to ADCs. According to [13], the analytical development of INL as a yield metric begins with [9], where the maximum deviation of the INL is introduced as a measure for distinguishing between good and bad current-steering DACs. Later in [10]- [13], we see a progression of refinements aimed towards improving the statistical accuracy of INL based yield estimates.…”

Section: Introductionmentioning

confidence: 99%

“…According to [13], the analytical development of INL as a yield metric begins with [9], where the maximum deviation of the INL is introduced as a measure for distinguishing between good and bad current-steering DACs. Later in [10]- [13], we see a progression of refinements aimed towards improving the statistical accuracy of INL based yield estimates. However, none of these works [9]- [13] offer a detailed comparison between INL yield measurements and other performance metrics such as signal-to-noise-plus-distortion ratio (SNDR), and it is unclear how to precisely interpret INL based yield estimates when targeting a specific ENOB yield for ADCs and DACs.…”

Section: Introductionmentioning

confidence: 99%

“…Later in [10]- [13], we see a progression of refinements aimed towards improving the statistical accuracy of INL based yield estimates. However, none of these works [9]- [13] offer a detailed comparison between INL yield measurements and other performance metrics such as signal-to-noise-plus-distortion ratio (SNDR), and it is unclear how to precisely interpret INL based yield estimates when targeting a specific ENOB yield for ADCs and DACs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical Analysis of ENOB and Yield in Binary Weighted ADCs and DACS With Random Element Mismatch

Fredenburg

Flynn

2012

IEEE Trans. Circuits Syst. I

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Mismatch motivates many of the design decisions for binary weighted, ratiometric converters, such as successive approximation (SAR) analog-to-digital converters (ADC), but the statistical relationship between mismatch and signal-to-noise-plus-distortion ratio (SNDR) has not been precisely quantified. In this paper, we analyze the effects of capacitor mismatch in a binary weighted, charge redistribution SAR ADC and derive a new analytic expression relating capacitor mismatch and the effective-number-of-bits (ENOB). We then explore the statistics of this new expression and develop a model that accurately predicts yield in terms of ENOB. Finally, the major results of this paper are generalized into a simple and compact design equation that relates resolution, mismatch, and ENOB to yield for all binary weighted, ratiometric converters. The expressions derived in this paper offer practical insight into the relationship between mismatch and performance for all binary, weighted ratiometric converters with these results validated through numerical simulations.

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