Design of mixed-signal systems-on-a-chip

Kundert, Ken; Chang, Henry; Jefferies, D.; Lamant, Gilles; Malavasi, E.; Sendig, F.

doi:10.1109/43.898832

Cited by 108 publications

(46 citation statements)

References 39 publications

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“…They have used two AMS examples to validate their framework, an oscillator made up of inverter chain and a complex mixed-signal fuzzy controller. The results were compared to other tools such as SPICE [9] and Spectre [27] achieving good results.…”

Section: Related Workmentioning

confidence: 99%

Analogue and mixed-signal extension to SystemC

Al-Junaid

Kázmierski

2005

IEE Proc., Circuits Devices Syst.

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Abstract-This paper presents a new methodology that enables extensions of SystemC to the analogue domain and allows modelling of mixed-signal and mixed energy-domain systems at arbitrary levels of abstraction. The new language constructs support analogue system variables, analogue components and user defined ordinary differential and algebraic equations. Support for digital-analogue interfaces has been provided for smooth integration of digital and analogue parts. Associated issues such as dealing with extremely small and zero time-step sizes have been addressed. A novel implementation of the lock-step mixed-signal synchronisation method to integrate the analogue kernel with the digital one has been proposed. Operation of the extended, mixedsignal simulation platform, named SystemC-A, is demonstrated using a suite of numerically difficult AMS examples including a practical, mixed-signal example of a PLL frequency multiplier with large-signal noise and jitter.

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Section: Related Workmentioning

confidence: 99%

Analogue and mixed-signal extension to SystemC

Al-Junaid

Kázmierski

2005

IEE Proc., Circuits Devices Syst.

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“…At the same time, however, shrinking process technologies generate a need to consider low-level physicals effects e.g. transistor-level effects, details of implementation, parasitic effects, or signal integrity [57]. Consequently, simple heuristic macromodels, such as e.g.…”

Section: Motivationmentioning

confidence: 99%

Nonlinear Dynamical Systems

Diks¹

1999

Nonlinear Time Series Analysis

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In this study we discuss the problem of Model Order Reduction (MOR) for a class of nonlinear dynamical systems. In particular, we consider reduction schemes based on projection of the original state-space to a lower-dimensional space e.g. by using Krylov methods. In the nonlinear case, however, applying a projection-based MOR scheme does not immediately yield computationally efficient macromodels. In order to overcome this fundamental problem, we propose to first approximate the original nonlinear system with a weighted combination of a small set of linearized models of this system, and then reduce each of the models with an appropriate projection method. The linearized models are generated about a state trajectory of the nonlinear system corresponding to a certain 'training' input.As demonstrated by results of numerical tests, the obtained trajectory quasi-piecewise-linear reduced order models are very cost-efficient, while providing superior accuracy as compared to existing MOR schemes, based on single-state Taylor's expansions. In this dissertation, the proposed MOR approach is tested for a number of examples of nonlinear dynamical systems, including micromachined devices, analog circuits (discrete transmission line models, operational amplifiers), and fluid flow problems. The tests validate the extracted models and indicate that the proposed approach can be effectively used to obtain system-level models for strongly nonlinear devices.This dissertation also shows an inexpensive method of generating trajectory piecewise-linear (TPWL) models based on constructing the reduced models 'on-the-fly', which accelerates simulation of the system response. Moreover, we propose a procedure for estimating simulation errors, which can be used to determine accuracy of the extracted trajectory piecewise-linear reduced order models. Finally, we present projection schemes which result in improved accuracy of the reduced order TPWL models, as well as discuss approaches leading to guaranteed stable and passive TPWL reduced-order models. AcknowledgmentsFirst, I would like to thank Professor Jacob White, my advisor for the last four years, for introducing me to the world of nonlinear simulation and modeling. Learning from him, especially learning how to ask the right questions, has proved an invaluable experience. His guidance and encouragement made this work possible.I am also very grateful to my thesis committee members, Professors Karen Willcox and Alexandre Megretski for the numerous, fruitful, and often very animated discussions on nonlinear dynamical systems and model order reduction, which substantially deepened my understanding of this problem and its challenges, and their important suggestions which contributed to improving and broadening this dissertation.I would like to thank Luca Daniel and Prof. John Wyatt for interesting discussions on passivity preservation for nonlinear reduced order models, as well as Prof. Jaime Peraire, Prof. Steven Senturia, and Dr. Joel Phillips from Cadence, for their valuable commen...

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“…In the microsystem presented here a microcontroller with embedded software and RAM was implemented to provide greater control and flexibility. With the increasing complexity of instrumentation circuits, it was desirable to use a system-on-chip (SoC) design methodology, in which the whole circuit, including all digital and mixed-signal components, were integrated on to a single piece of silicon [14]. In such a heterogeneous design where hardware and embedded software co-exist, hardware intellectual property (IP) blocks can be implemented wherever the software cannot meet the timing constraints, e.g., in real-time data encoding and authentication.…”

Section: Design Considerationsmentioning

confidence: 99%

A Programmable Microsystem Using System-on-Chip for Real-time Biotelemetry

Wang

Johannessen

Hammond

et al. 2005

IEEE Trans. Biomed. Eng.

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Abstract-A telemetry microsystem, including multiple sensors, integrated instrumentation and a wireless interface has been implemented. We have employed a methodology akin to that for System-on-Chip microelectronics to design an integrated circuit instrument containing several "intellectual property" blocks that will enable convenient reuse of modules in future projects. The present system was optimized for low-power and included mixed-signal sensor circuits, a programmable digital system, a feedback clock control loop and RF circuits integrated on a 5 mm 5 mm silicon chip using a 0.6 m, 3.3 V CMOS process. Undesirable signal coupling between circuit components has been investigated and current injection into sensitive instrumentation nodes was minimized by careful floor-planning. The chip, the sensors, a magnetic induction-based transmitter and two silver oxide cells were packaged into a 36 mm 12 mm capsule format. A base station was built in order to retrieve the data from the microsystem in real-time. The base station was designed to be adaptive and timing tolerant since the microsystem design was simplified to reduce power consumption and size. The telemetry system was found to have a packet error rate of 10 3 using an asynchronous simplex link. Trials in animal carcasses were carried out to show that the transmitter was as effective as a conventional RF device whilst consuming less power.

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Design of mixed-signal systems-on-a-chip

Cited by 108 publications

References 39 publications

Analogue and mixed-signal extension to SystemC

Analogue and mixed-signal extension to SystemC

Nonlinear Dynamical Systems

A Programmable Microsystem Using System-on-Chip for Real-time Biotelemetry

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