Parameterized macromodeling of passive and active dynamical systems

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

Layout-accurate Ultra-fast System-level Design Exploration Through Verilog-ams

This research addresses problems in designing analog and mixed-signal (AMS) systems by bridging the gap between system-level and circuit-level simulation by making simulations fast like system-level and accurate like circuit-level. The tools proposed include metamodel integrated Verilog-AMS based design exploration flows. The research involves design centering, metamodel generation flows for creating efficient behavioral models, and Verilog-AMS integration techniques for model realization. The core of the proposed solution is transistor-level and layout-level metamodeling and their incorporation in Verilog-AMS. Metamodeling is used to construct efficient and layout-accurate surrogate models for AMS system building blocks. Verilog-AMS, an AMS hardware description language, is employed to build surrogate model implementations that can be simulated with industrial standard simulators. The case-study circuits and systems include an operational amplifier (OP-AMP), a voltage-controlled oscillator (VCO), a charge-pump phase-locked loop (PLL), and a continuous-time delta-sigma modulator (DSM). The minimum and maximum error rates of the proposed OP-AMP model are 0.11 % and 2.86 %, respectively. The error rates for the PLL lock time and power estimation are 0.7 % and 3.0 %, respectively. The OP-AMP optimization using the proposed approach is ~17000× faster than the transistor-level model based approach. The optimization achieves a ~4× power reduction for the OP-AMP design. The PLL parasitic-aware optimization achieves a 10× speedup and a 147 µW power reduction. Thus the experimental results validate the effectiveness of the proposed solution

Modeling and Simulation of Nonlinearly Loaded Electromagnetic Systems via Reduced Order Models - A Case Study: Energy Selective Surfaces

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

A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression

This paper introduces a probabilistic machine learning framework for the uncertainty quantification (UQ) of electronic circuits based on Gaussian process regression (GPR). As opposed to classical surrogate modeling techniques, GPR inherently provides information on the model uncertainty. The main contribution of this work is twofold. First, it describes how, in an UQ scenario, the model uncertainty can be combined with the uncertainty of the input design parameters to provide confidence bounds for the statistical estimates of the system outputs, such as moments and probability distributions. These confidence bounds allows assessing the accuracy of the predicted statistics. Second, in order to deal with dynamic multi-output systems, principal component analysis (PCA) is effectively employed to compress the time-dependent output variables into a smaller set of components, for which the training of individual GPR models becomes feasible. The uncertainty on the principal components is then propagated back to the original output variables. Several application examples, ranging from a trivial RLC circuit to real-life designs, are used to illustrate and validate the advocated approach

Tensor Computation: A New Framework for High-Dimensional Problems in EDA

Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g. full-chip routing/placement and circuit sizing), or extensive process variations (e.g. variability/reliability analysis and design for manufacturability). The computational challenges generated by such high dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents "tensor computation" as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and System

High-dimensional data driven parameterized macromodeling

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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

Modeling for the Computer-Aided Design of Long Interconnects

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