An automatic coefficient design methodology for high-order bandpass sigma-delta modulator with single-stage structure

Wang, Hwi-Ming; Kuo, Tai‐Haur

doi:10.1109/tcsii.2006.875304

Cited by 9 publications

(3 citation statements)

References 7 publications

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“…In Kuo et al (1999), Bajdechi et al (2004), Wang and Kuo (2006) and Tang and Doboli (2006), the assumption of independent Gaussian distribution of the signal path coefficient in the Delta-Sigma modulator may not be realistic, since the coefficients are implemented in different circuits that are subject to variations of common process parameters. Also, the circuit-level non-idealities should be considered for more accuracy since the real SNR variation is dependent on them.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Besides, the variation of design variables (such as resistors and capacitors) are not related to process parameter variations. Similar work has been also reported on statistical analysis of Delta-Sigma modulators (Bajdechi, Gielen, & Huijsing, 2004;Kuo, Chen, & Chen, 1999;Tang, 2007b;Tang & Doboli, 2006;Wang & Kuo, 2006;Webb & Tang, 2008). An ideal behavioural model, such as the one shown in Figure 1 for a fourth-order continuous-time Delta-Sigma modulator, is used.…”

Section: Introduction and Previous Workmentioning

confidence: 87%

“…In this case, SNR also follows Gaussian distribution due to the linear equation. Kuo et al (1999), Bajdechi et al (2004), Wang and Kuo (2006) and Tang and Doboli (2006) assume random Gaussian or uniform distributions for all signal path coefficients and directly apply the Monte-Carlo method for statistical analysis. However, the above approaches do not relate performance parameter variations to fundamental process parameter variations plus many circuit-level nonidealities are not considered; therefore, the statistical analysis in those approaches may not be realistic.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

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Hierarchical statistical analysis of complex analog and mixed-signal systems

Webb

Tang

2014

International Journal of Electronics

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With increasing process parameter variations in nanometre regime, circuits and systems encounter significant performance variations and therefore statistical analysis has become increasingly important. For complex analog and mixed-signal circuits and systems, efficient yet accurate statistical analysis has been a challenge mainly due to significant simulation and modelling time. In the past years, there have been various approaches proposed for statistical analysis of analog and mixed-signal circuits. A recent work is reported to address statistical analysis for continuous-time Delta-Sigma modulators. In this article, we generalise that method and present a hierarchical method for efficient statistical analysis of complex analog and mixed-signal circuits while maintaining reasonable accuracy. At circuit level, we use the response surface modelling method to extract quadratic models of circuit-level performance parameters in terms of process parameters. Then at system level, we use behavioural models and apply the Monte-Carlo method for statistical evaluation of system performance parameters. We illustrate and validate the method on a continuous-time Delta-Sigma modulator and an analog filter.

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