Stochastic macromodeling for hierarchical uncertainty quantification of nonlinear electronic systems

A hierarchical stochastic macromodeling approach is proposed for the efficient variability analysis of complex nonlinear electronic systems. A combination of the Transfer Function Trajectory and Polynomial Chaos methods is used to generate stochastic macromodels. In order to reduce the computational complexity of the model generation when the number of stochastic variables increases, a hierarchical system decomposition is used. Pertinent numerical results validate the proposed methodology

Parametric Macromodels of Differential Drivers and Receivers

This paper addresses the modeling of differential drivers and receivers for the analog simulation of high-speed interconnection systems. The proposed models are based on mathematical expressions, whose parameters can be estimated from the transient responses of the modeled devices. The advantages of this macromodeling approach are: improved accuracy with respect to models based on simplified equivalent circuits of devices; improved numerical efficiency with respect to detailed transistor-level models of devices; hiding of the internal structure of devices; straightforward circuit interpretation; or implementations in analog mixed-signal simulators. The proposed methodology is demonstrated on example devices and is applied to the prediction of transient waveforms and eye diagrams of a typical low-voltage differential signaling (LVDS) data link

Review of polynomial chaos-based methods for uncertainty quantification in modern integrated circuits

Advances in manufacturing process technology are key ensembles for the production of integrated circuits in the sub-micrometer region. It is of paramount importance to assess the effects of tolerances in the manufacturing process on the performance of modern integrated circuits. The polynomial chaos expansion has emerged as a suitable alternative to standardMonte Carlo-based methods that are accurate, but computationally cumbersome. This paper provides an overview of the most recent developments and challenges in the application of polynomial chaos-based techniques for uncertainty quantification in integrated circuits, with particular focus on high-dimensional problems

Reliable Eye-Diagram Analysis of Data Links via Device Macromodels

This paper addresses the impact of device macromodels on the accuracy of signal integrity and performance predictions for critical digital interconnecting systems. It exploits nonlinear parametric models for both single-ended and differential devices, including the effects of power supply fluctuations and receiver bit detection. The analysis demonstrates that the use of well-designed macromodels dramatically speeds up the simulation as well it preserves timing accuracy even for long bit sequences

Parameterized macromodeling of passive and active dynamical systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Modeling, Optimization and Testing for Analog/Mixed-Signal Circuits in Deeply Scaled CMOS Technologies

As CMOS technologies move to sub-100nm regions, the design and verification for analog/mixed-signal circuits become more and more difficult due to the problems including the decrease of transconductance, severe gate leakage and profound mismatches. The increasing manufacturing-induced process variations and their impacts on circuit performances make the already complex circuit design even more sophisticated in the deeply scaled CMOS technologies. Given these barriers, efforts are needed to ensure the circuits are robust and optimized with consideration of parametric variations. This research presents innovative computer-aided design approaches to address three such problems: (1) large analog/mixed-signal performance modeling under process variations, (2) yield-aware optimization for complex analog/mixedsignal systems and (3) on-chip test scheme development to detect and compensate parametric failures. The first problem focus on the efficient circuit performance evaluation with consideration of process variations which serves as the baseline for robust analog circuit design. We propose statistical performance modeling methods for two popular types of complex analog/mixed-signal circuits including Sigma-Delta ADCs and charge-pump PLLs. A more general performance modeling is achieved by employing a geostatistics motivated performance model (Kriging model), which is accurate and efficient for capturing stand-alone analog circuit block performances. Based on the generated block-level performance models, we can solve the more challenging problem of yield-aware system optimization for large analog/mixed-signal systems. Multi-yield pareto fronts are utilized in the hierarchical optimization framework so that the statistical optimal solutions can be achieved efficiently for the systems. We further look into on-chip design-for-test (DFT) circuits in analog systems and solve the problems of linearity test in ADCs and DFT scheme optimization in charge-pump PLLs. Finally a design example of digital intensive PLL is presented to illustrate the practical applications of the modeling, optimization and testing approaches for large analog/mixed-signal systems

Layout-level Circuit Sizing and Design-for-manufacturability Methods for Embedded RF Passive Circuits

The emergence of multi-band communications standards, and the fast pace of the consumer electronics markets for wireless/cellular applications emphasize the need for fast design closure. In addition, there is a need for electronic product designers to collaborate with manufacturers, gain essential knowledge regarding the manufacturing facilities and the processes, and apply this knowledge during the design process. In this dissertation, efficient layout-level circuit sizing techniques, and methodologies for design-for-manufacturability have been investigated. For cost-effective fabrication of RF modules on emerging technologies, there is a clear need for design cycle time reduction of passive and active RF modules. This is important since new technologies lack extensive design libraries and layout-level electromagnetic (EM) optimization of RF circuits become the major bottleneck for reduced design time. In addition, the design of multi-band RF circuits requires precise control of design specifications that are partially satisfied due to manufacturing variations, resulting in yield loss. In this work, a broadband modeling and a layout-level sizing technique for embedded inductors/capacitors in multilayer substrate has been presented. The methodology employs artificial neural networks to develop a neuro-model for the embedded passives. Secondly, a layout-level sizing technique for RF passive circuits with quasi-lumped embedded inductors and capacitors has been demonstrated. The sizing technique is based on the circuit augmentation technique and a linear optimization framework. In addition, this dissertation presents a layout-level, multi-domain DFM methodology and yield optimization technique for RF circuits for SOP-based wireless applications. The proposed statistical analysis framework is based on layout segmentation, lumped element modeling, sensitivity analysis, and extraction of probability density functions using convolution methods. The statistical analysis takes into account the effect of thermo-mechanical stress and process variations that are incurred in batch fabrication. Yield enhancement and optimization methods based on joint probability functions and constraint-based convex programming has also been presented. The results in this work have been demonstrated to show good correlation with measurement data.Ph.D.Committee Chair: Swaminathan, Madhavan; Committee Member: Fathianathan, Mervyn; Committee Member: Lim, Sung Kyu; Committee Member: Peterson, Andrew; Committee Member: Tentzeris, Mano