MODELING AND SPICE IMPLEMENTATION OF SILICON-ON-INSULATOR (SOI) FOUR GATE (G4FET) TRANSISTOR

As the device dimensions have reduced from micrometer to nanometer range, new bulk silicon devices are now facing many undesirable effects of scaling leading device engineers to look for new process technologies. Silicon-on-insulator (SOI) has emerged as a very promising candidate for resolving the major problems plaguing the bulk silicon technology. G4FET [G4FET] is a SOI transistor with four independent gates. Although G4FET has already shown great potential in different applications, the widespread adoption of a technology in circuit design is heavily dependent upon good SPICE (Simulation Program with Integrated Circuit Emphasis) models. CAD (Computer Aided Design) tools are now ubiquitous in circuit design and a fast, robust and accurate SPICE model is absolutely necessary to transform G4FET into a mainstream technology. The research goal is to develop suitable SPICE models for G4FET to aid circuit designers in designing innovative analog and digital circuits using this new transistor. The first phase of this work is numerical modeling of the G4FET where four different numerical techniques are implemented, each with its merits and demerits. The first two methods are based on multivariate Lagrange interpolation and multidimensional Bernstein polynomial. The third numerical technique is based on multivariate regression polynomial to aid modeling with dense gridded data. Another suitable alternative namely multidimensional linear and cubic spline interpolation is explored as the fourth numerical modeling approach to solve some of the problems resulting from single polynomial approximation. The next phase of modeling involves developing a macromodel combining already existing SPICE models of MOSFET (metal–oxide–semiconductor field-effect transistor) and JFET (junction-gate field-effect transistor). This model is easy to implement in circuit simulators and provides good results compared to already demonstrated experimental works with innovative G4FET circuits. The final phase of this work involves the development of a physics-based compact model of G4FET with some empirical fitting parameters. A model for depletion-all-around operation is implemented in circuit simulator based on previous work. Another simplified model, combining MOS and JFET action, is implemented in circuit simulator to model the accumulation mode operation of G4FET

MODEL ORDER REDUCTION OF NONLINEAR DYNAMIC SYSTEMS USING MULTIPLE PROJECTION BASES AND OPTIMIZED STATE-SPACE SAMPLING

Model order reduction (MOR) is a very powerful technique that is used to deal with the increasing complexity of dynamic systems. It is a mature and well understood field of study that has been applied to large linear dynamic systems with great success. However, the continued scaling of integrated micro-systems, the use of new technologies, and aggressive mixed-signal design has forced designers to consider nonlinear effects for more accurate model representations. This has created the need for a methodology to generate compact models from nonlinear systems of high dimensionality, since only such a solution will give an accurate description for current and future complex systems.The goal of this research is to develop a methodology for the model order reduction of large multidimensional nonlinear systems. To address a broad range of nonlinear systems, which makes the task of generalizing a reduction technique difficult, we use the concept of transforming the nonlinear representation into a composite structure of well defined basic functions from multiple projection bases.We build upon the concept of a training phase from the trajectory piecewise-linear (TPWL) methodology as a practical strategy to reduce the state exploration required for a large nonlinear system. We improve upon this methodology in two important ways: First, with a new strategy for the use of multiple projection bases in the reduction process and their coalescence into a unified base that better captures the behavior of the overall system; and second, with a novel strategy for the optimization of the state locations chosen during training. This optimization technique is based on using the Hessian of the system as an error bound metric.Finally, in order to treat the overall linear/nonlinear reduction task, we introduce a hierarchical approach using a block projection base. These three strategies together offer us a new perspective to the problem of model order reduction of nonlinear systems and the tracking or preservation of physical parameters in the final compact model

SCEE 2008 book of abstracts : the 7th International Conference on Scientific Computing in Electrical Engineering (SCEE 2008), September 28 – October 3, 2008, Helsinki University of Technology, Espoo, Finland

This report contains abstracts of presentations given at the SCEE 2008 conference.reviewe

Electrical performance analysis of high-speed interconnects and circuits by numerical modeling methods

Ph.DDOCTOR OF PHILOSOPH

Incorporation of feed-network and circuit modeling into the time-domain finite element analysis of antenna arrays and microwave circuits

In this dissertation, accurate and efficient numerical algorithms are developed to incorporate the feed-network and circuit modeling into the time-domain finite element analysis of antenna arrays and microwave circuits. First, simulation of an antenna system requires accurate modeling of interactions between the radiating elements and the associated feeding network. In this work, a feed network is represented in terms of its scattering matrix in a rational function form in the frequency domain that enables its interfacing with the time-domain finite element modeling of the antenna elements through a fast recursive time-convolution algorithm. The exchange of information between the antenna elements and the feed network occurs through the incident and reflected modal voltages/currents at properly defined port interfaces. The proposed numerical scheme allows a full utilization of the advanced antenna simulation techniques, and significantly extends the current antenna modeling capability to the system level. Second, a hybrid field-circuit solver that combines the capabilities of the time-domain finite element method and a lumped circuit analysis is developed for accurate and efficient characterization of complicated microwave circuits that include both distributive and lumped-circuit components. The distributive portion of the device is modeled by the time-domain finite element method to generate a finite element subsystem, while the lumped circuits are analyzed by a SPICE-like circuit solver to generate a circuit subsystem. A global system for both the finite-element and circuit unknowns is established by combining the two subsystems through coupling matrices to model their interactions. For simulations of even more complicated mixed-scale circuit systems that contain pre-characterized blocks of discrete circuit elements, the hybrid field-circuit analysis implemented a systematic and efficient algorithm to incorporate multiport lumped networks in terms of frequency-dependent admittance matrices. Other advanced features in the hybrid field-circuit solver include application of the tree-cotree splitting algorithm and introduction of a flexible time-stepping scheme. Various numerical examples are presented to validate the implementation and demonstrate the accuracy, efficiency, and applications of the proposed numerical algorithms

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Stability-preserving model reduction for linear and nonlinear systems arising in analog circuit applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 221-229).Despite the increasing presence of RF and analog components in personal wireless electronics, such as mobile communication devices, the automated design and optimization of such systems is still an extremely challenging task. This is primarily due to the presence of both parasitic elements and highly nonlinear elements, which makes simulation computationally expensive and slow. The ability to generate parameterized reduced order models of analog systems could serve as a first step toward the automatic and accurate characterization of geometrically complex components and subcircuits, eventually enabling their synthesis and optimization. This thesis presents techniques for reduced order modeling of linear and nonlinear systems arising in analog applications. Emphasis is placed on developing techniques capable of preserving important system properties, such as stability, and parameter dependence in the reduced models. The first technique is a projection-based model reduction approach for linear systems aimed at generating stable and passive models from large linear systems described by indefinite, and possibly even mildly unstable, matrices. For such systems, existing techniques are either prohibitively computationally expensive or incapable of guaranteeing stability and passivity. By forcing the reduced model to be described by definite matrices, we are able to derive a pair of stability constraints that are linear in terms of projection matrices.(cont.) These constraints can be used to formulate a semidefinite optimization problem whose solution is an optimal stabilizing projection framework. The second technique is a projection-based model reduction approach for highly nonlinear systems that is based on the trajectory piecewise linear (TPWL) method. Enforcing stability in nonlinear reduced models is an extremely difficult task that is typically ignored in most existing techniques. Our approach utilizes a new nonlinear projection in order to ensure stability in each of the local models used to describe the nonlinear reduced model. The TPWL approach is also extended to handle parameterized models, and a sensitivity-based training system is presented that allows us to efficiently select inputs and parameter values for training. Lastly, we present a system identification approach to model reduction for both linear and nonlinear systems. This approach utilizes given time-domain data, such as input/output samples generated from transient simulation, in order to identify a compact stable model that best fits the given data. Our procedure is based on minimization of a quantity referred to as the 'robust equation error', which, provided the model is incrementally stable, serves as up upper bound for a measure of the accuracy of the identified model termed 'linearized output error'. Minimization of this bound, subject to an incremental stability constraint, can be cast as a semidefinite optimization problem.by Bradley Neil Bond.Ph.D