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A 1.8V digitalaudio sigmadelta modulator in 0.8µm CMOS
 IEEE Journal of SolidState Circuits
, 1997
"... Abstract — Oversampling techniques based on sigmadelta (ΣΔ) modulation offer numerous advantages for the realization of highresolution analogtodigital (A/D) converters in a lowvoltage environment. This paper examines the design and implementation of a CMOS ΣΔ modulator for digitalaudio A/D con ..."
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Abstract — Oversampling techniques based on sigmadelta (ΣΔ) modulation offer numerous advantages for the realization of highresolution analogtodigital (A/D) converters in a lowvoltage environment. This paper examines the design and implementation of a CMOS ΣΔ modulator for digitalaudio A/D conversion that operates from a single 1.8V power supply. A cascaded modulator that maintains a large fullscale input range while avoiding signal clipping at internal nodes is introduced. The experimental modulator has been designed with fullydifferential switchedcapacitor integrators employing different input and output commonmode levels and boosted clock drivers in order to facilitate low voltage operation. Precise control of commonmode levels, high power supply noise rejection, and low power dissipation are obtained through the use of twostage, class A/AB operational amplifiers. At a sampling rate of 4 MHz and an oversampling ratio of 80, an implementation of the modulator in a 0.8μm CMOS technology with metaltopolycide capacitors and NMOS and PMOS threshold voltages of +0.65V and –0.75V, respectively, achieves a dynamic range of 99 dB at a Nyquist conversion rate of 50 kHz. The modulator can operate from supply voltages ranging from 1.5 V to 2.5 V, occupies an active area of 1.5 mm 2, and dissipates 2.5 mW from a 1.8V supply.
Hierarchical Statistical Characterization of MixedSignal Circuits Using Behavior Modeling
 Proc. Of ICCAD
, 1996
"... A methodology for hierarchical statistical circuit characterization which does not rely upon circuitlevel Monte Carlo simulation is presented. The methodology uses principal component analysis, response surface methodology, and statistics to directly calculate the statistical distributions of high ..."
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Cited by 18 (2 self)
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A methodology for hierarchical statistical circuit characterization which does not rely upon circuitlevel Monte Carlo simulation is presented. The methodology uses principal component analysis, response surface methodology, and statistics to directly calculate the statistical distributions of higherlevel parameters from the distributions of lowerlevel parameters. We have used the methodology to characterize a folded cascode operational amplifier and a phaselocked loop. This methodology permits the statistical characterization of large analog and mixedsignal systems, many of which are extremely timeconsuming or impossible to characterize using existing methods. 1
From system specification to layout: seamless topdown design methods for analog and mixedsignal applications
 Proceedings of Design, Automation and Test in Europe Conference and Exhibition
, 2002
"... Design automation for analog/mixedsignal (A/MS) circuits and systems is still lagging behind compared to what has been reached in the digital area. As SystemonChip (SoC) designs include analog components in most cases, these analog parts become even more a bottleneck in the overall design process ..."
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Cited by 5 (0 self)
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Design automation for analog/mixedsignal (A/MS) circuits and systems is still lagging behind compared to what has been reached in the digital area. As SystemonChip (SoC) designs include analog components in most cases, these analog parts become even more a bottleneck in the overall design process. The paper is dedicated to latest R&D activities within the MEDEA+ project ANASTASIA+. Main focus will be the development of seamless topdown design methods for integrated analog and mixedsignal systems and to achieve a high level of automation and reuse in the A/MS design process. These efforts are motivated by the urgent need to close the current gap in the industrial design flow between system specification and design on the one hand and blocklevel circuit design on the other hand. The paper will focus on three subtopics starting with the topdown design flow with applications from circuit sizing, design centering, and automated behavioral modeling. The next part focuses on modeling and simulation of specific functionalities in sigmadelta design while the last section is dedicated to a mixedsignal SystemonChip design environment. 1.
On Incremental SigmaDelta Modulation with Optimal Filtering
 ACCEPTED FOR PUBLICATION TCASI
, 2005
"... The paper presents a quantizationtheoretic framework for studying incremental Σ∆ quantization systems. The framework makes it possible to efficiently compute the quantization intervals and hence the transfer function of the quantizer, and to determine the mean square error (MSE) and maximum error f ..."
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Cited by 4 (0 self)
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The paper presents a quantizationtheoretic framework for studying incremental Σ∆ quantization systems. The framework makes it possible to efficiently compute the quantization intervals and hence the transfer function of the quantizer, and to determine the mean square error (MSE) and maximum error for the optimal and conventional linear filters for first and second order incremental Σ∆ modulators. The results show that the optimal filter can significantly outperform conventional linear filters in terms of both MSE and maximum error. The performance of conventional Σ∆ quantizers is then compared to that of incremental Σ∆ with optimal filtering for bandlimited signals. It is shown that incremental Σ∆ can outperform the conventional approach in terms of signal to noise+distortion ratio (SNDR) and the characteristics of the power spectral density (PSD). The framework is also used to provide a simpler and more intuitive derivation of the Zoomer algorithm.
On Incremental SigmaDelta Modulation with
"... The paper presents a quantizationtheoretic framework for studying incremental Σ ∆ data conversion systems. The framework makes it possible to efficiently compute the quantization intervals and hence the transfer function of the quantizer, and to determine the mean square error (MSE) and maximum err ..."
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The paper presents a quantizationtheoretic framework for studying incremental Σ ∆ data conversion systems. The framework makes it possible to efficiently compute the quantization intervals and hence the transfer function of the quantizer, and to determine the mean square error (MSE) and maximum error for the optimal and conventional linear filters for first and second order incremental Σ ∆ modulators. The results show that the optimal filter can significantly outperform conventional linear filters in terms of both MSE and maximum error. The performance of conventional Σ ∆ data converters is then compared to that of incremental Σ ∆ with optimal filtering for bandlimited signals. It is shown that incremental Σ ∆ can outperform the conventional approach in terms of signal to noise and distortion ratio (SNDR). The framework is also used to provide a simpler and more intuitive derivation of the Zoomer algorithm.
Representative of Graduate Studies
, 2006
"... Sigma delta modulators (ΣΔMs) form part of the core of today’s mixedsignal designs. They are cornerstone components of oversampled data converters. Such data converters take advantage of digital signal processing techniques and VLSI technology to provide high performance with low sensitivity to an ..."
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Sigma delta modulators (ΣΔMs) form part of the core of today’s mixedsignal designs. They are cornerstone components of oversampled data converters. Such data converters take advantage of digital signal processing techniques and VLSI technology to provide high performance with low sensitivity to analog component imperfections and noisy conditions. The ongoing research on these devices shows the potential of ΣΔ data converters as a promising candidate for highspeed, highresolution, and lowpower mixedsignal interfaces. As for any electronic system, efficient modeling and simulation tools are essential in the design cycle of oversampled converters, particularly for their embedded ΣΔMs. Although transistorlevel simulation is the most accurate approach known for these components, this method becomes impractical for complex systems due to the long computational time required. This situation has led circuit designers to consider alternate modeling techniques. Among these, the simulation of ΣΔMs using behavioral models has become the focus of attention for a large portion of the design community. This thesis presents an accurate behav
Elettronica ed Informatica
"... A methodology for hierarchical statistical circuit characterization which does not rely upon circuitlevel Monte Carlo simulation is presented. The methodology uses principal component analysis, response surface methodology, and statistics to directly calculate the statistical distributions of highe ..."
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A methodology for hierarchical statistical circuit characterization which does not rely upon circuitlevel Monte Carlo simulation is presented. The methodology uses principal component analysis, response surface methodology, and statistics to directly calculate the statistical distributions of higherlevel parameters from the distributions of lowerlevel parameters. We have used the methodology to characterize a folded cascode operational amplifier and a phaselocked loop. This methodology permits the statistical characterization of large analog and mixedsignal systems, many of which are extremely timeconsuming or impossible to characterize using existing methods. 1