Graduate.
Graduate Course Descriptions211. Quantum Mechanics. Discussion of wave mechanics including elementary applications, free particle dynamics, Schroedinger equation including treatment of systems with exact solutions, and approximate methods for time-dependent quantum mechanical systems with emphasis on quantum phenomena underlying solid-state electronics and physics. Prerequisite: Differential equations.
214. Introduction to Solid-State Physics. Discussion of solid-state phenomena including crystalline structures, x-ray and particle diffraction in crystals, lattice dynamics, free electron theory in metals, energy bands in solids, and superconductivity with emphasis on understanding the electrical and optical properties of solids.
215. Semiconductor Physics. A Quantitative treatment of the physical processes that underlie semiconductor device operation. Topics include band theory and conduction phenomena; equilibrium and nonequilibrium charge carrier distributions; charge generation, injection, and recombination; drift and diffusion processes. Prerequisite: EE211 or consent of instructor.
216. Devices for Integrated Circuits. Derivation of basic semiconductor properties such as the effective mass, effective density of states, SHR recombination, avalanche breakdown and energy-band diagrams. Application of the continuity equation, Gauss' law, and Poisson's equation to obtain the I-V and C-V behavior of Si and GaAs p-n junctions and Schottky barriers, bipolar transistors, GaAs MOSFETs; and MOSFETs. Relation of device physics to SPICE parameters. Four laboratory exercises.
217. Analog Integrated Circuits. Analysis and design of analog integrated circuits. Bipolar and MOSFET circuits. SPICE models. Elementary integrated amplifier circuits, performance of operational amplifiers and other analog circuits including frequency response and noise. A/D converters and switched capacitor filters. Prerequisite: EE216.
218. Integrated Circuit Engineering. Basic processing techniques and layout technology for integrated circuits. Photolithography, diffusion, oxidation, ion implantation, and metalization. Design, fabrication and testing of integrated circuits. Prerequisite: EE216. One course.
219. Digital Integrated Circuits. Analysis and design of digital integrated circuits. IC Technology. Switching characteristics and power consumption in MOS devices, bipolar devices and interconnects. Analysis of digital circuits implemented in NMOS, CMOS, TTL, ECL, and BiCMOS. Propagation delay modeling. Analysis of logic (inverters, gates) and memory (SRAM, DRAM) circuits. Influence of technology and device structure on performance and reliability of digital ICs, SPICE modeling. Prerequisites: EE151 and 216.
241. Non-Linear Control Systems: Analysis of the qualitative behavior of nonlinear systems and the design and synthesis of controllers for such systems. State space methods of stability analysis including the stability in the small, stability in the large, linearization and Lyapunov's direct method. Input-output stability and the Small Gain Theorem. Absolute stability (including Circle and Popov Criteria). Feedback stabilization of nonlinear control systems (via linearization). Applications to control system design. Optional topics include center manifolds, feedback linearization, bifurcations and chaos. Prerequisite: EE 141 or consent of instructor.
243. Pattern Classification and Recognition. Parameter estimation and supervised learning, nonparametric techniques, linear discriminant functions, clustering, language theory related to pattern recognition, examples from areas such as character and severe weather recognition, classification and community health data, recognition of geometrical configuration, algorithms for recognizing low resolution touch-sensor array signatures, and 3-D objects. Prerequisite: Basic probability theory and linear algebra.
245. Digital Control Systems. Review of traditional techniques used for the design of discrete time control system; introduction of nonclassical control problems of intelligent machines such as robots. Limitations of the assumptions required by traditional design and analysis tools used in automatic control. Prerequisites: Linear control systems and EE 282.
246. Optimal Control. Review of basic linear control theory and linear/nonlinear programming. Dynamic programming and the Hamilton-Jacobi-Bellman Equation. Calculus of variations. Hamiltonian and costatic equations. Pontryagin�s Minimum Principle. Solution to common constrained optimization problems. Prerequisite: EE141 or equivalent.
251. Advanced Digital System Design. Theory and hands-on experience and advanced digital system design. High-speed design, high complexity design (more than 10,000 gates), implementation technology selection, system modeling, power and clock distribution, line termination, and cooling. Case studies and demonstrations. Extensive use of CAD tools for logic minimization, logic synthesis, and system simulation. Rapid system prototyping with off-the-shelf and custom components. Laboratory exercises and a semester project. Prerequisites: Switching theory and logic design, and electronics.
252. Advanced Computer Architecture. A second course on computer architecture. Definition of high-performance computing. The von Neumann bottleneck, Amdahl's law. Computer taxonomies. Memory organization, Princeton/Harvard architectures, caches, and virtual memory. Instruction pipelining. Vector processing. Instruction sets (RISC/CISC/VLIW). Parallel processing (SIMD/MIMD). Multiprocessor interconnection networks, communications, and synchronization. Prerequisite: Introductory computer architecture.
253. Parallel System Performance. Intrinsic limitations to computer performance. Amdahl's Law and its extensions. Components of computer architecture and operating systems, and their impact on the performance available to applications. Intrinsic properties of application programs and their relation to performance. Task graph models of parallel programs. Estimation of best possible execution times. Task assignment and related heuristics. Load balancing. Specific examples from computationally intensive, I/O intensive, and mixed parallel and distributed computations. Global distributed system performance. Prerequisites: EE151 and 152; CPS 131.
254. Fault-Tolerant and Testable Computer Systems. Faults and failure mechanisms, test generation techniques and diagnostic program development for detection and location of faults in digital networks; design for testability, redundancy techniques, self-checking and fail-safe networks, fault-tolerant computer architectures. Prerequisite: Switching theory and logic design.
255. Mathematical Methods for Systems Analysis I. Basic concepts and techniques used in the stochastic modeling of systems. Elements of probability, statistics, queuing theory, and simulation. Prerequisite: Four semesters of college mathematics.
257. Performance and Reliability of Computer Networks. Methods of performance and reliability analysis of local area networks as well as wide area networks. Probabilistic analysis using Markov models, stochastic Petri nets, queuing networks and hierarchical models. Statistical analysis of measured data and optimization of network structures. Prerequisites: Introductory computer network architecture and EE255.
258. Artificial Neural Networks. Elementary biophysical background for signal propagation in natural neural systems. Artificial neural networks (ANN) and the history of computing; early work of McCulloch and Pitts, of Kleene, of von Neumann and others. The connectionist model. The random neural network model. ANN as universal computing machines. Associative memory. Learning. Algorithmic aspects of learning. Complexity Limitations. Applications to pattern recognition, image processing and combinational optimization. Prerequisite: EE151.
261. Introduction to VLSI Systems. A first course in VLSI design with CMOS technologies. A study of devices, circuits, fabrication technology; logic design techniques, subsystem design and system architectures. Modeling of circuits and subsystems. Testing of gates, subsystems and chips, and design for testability. The fundamental of full-custom design, and some semi-custom design. Prerequisites: Switching theory and logic design, and electronics.
262. Analog Integrated Circuit Design. Design and layout of CMOS analog integrated circuits. Qualitative theory of pn junctions, bipolar and MOS devices, and large and small signal models with emphasis on MOS technology. Variety of analog circuits focusing on continuous time operational amplifiers. Frequency response, stability, and compensation of the amplifier circuits. Complex analog subsystems such as phase-locked loops, A/D and D/A converters, switched capacitor simulation, layout, extraction, and verification and MATlb modeling. Projects using full custom VLSI CAD software. Prerequisite: EE261
266. VLSI Design Verification Techniques. VLSI verification tool design. Design and capabilities of circuit simulation, timing simulation, logic simulation, and functional simulation. Techniques applied in timing verification and other static verification tools. Parallel processing and its applications simulation. Physical design issues related to verification. Prerequisites: Working knowledge of C or Fortran and EE261.
269. VLSI Chip Testing. Introduction to VLSI chip and system testing. Testing theory, strategies, and fault identification. Hands-on testing experience with faulty chips and systems, chips designed in EE 261, and testing equipment available in the department. Prerequisite: EE261.
271. Electromagnetic Theory. The classical Theory of Maxwell�s equations; electrostatics, magnetostatics, boundary value problems including numerical solutions, currents and their interactions, and force and energy relations. Three class sessions. Prerequisite: consent of the instructor.
272. Electromagnetic Communication Systems. Review of fundamental laws of Maxwell, Gauss, Ampere, and Faraday. Elements of waveguide propagation and antenna radiation. Analysis of antenna arrays by images. Determination of gain, loss, and noise temperature parameters for terrestrial and satellite electromagnetic communication systems. Prerequisite: Introductory electomagnetic fields or EE271.
273. Optical Communication Systems. Mathematical methods, physical ideas, and device concepts of optoelectronics. Maxwell's equations, and definitions of energy density and power flow. Transmission and reflection of plane waves at interfaces. Optical resonators, waveguides, fibers, and detectors are also presented. Prerequisite: Introductory electromagnetic fields.
274. Modern Optics. Optical processes including the propagation of light, coherence, interference, and diffraction. Consideration of the optical properties of solids with applications of these concepts to lasers and modern optical devices.
275. Microwave Electronic Circuits. Microwave circuit analysis and design techniques. Properties of planar transmission lines for integrated circuits. Matrix and computer-aided methods for analysis and design of circuit components. Analysis and design of input, output, and interstage networks for microwave transistor amplifiers and oscillators. Topics on stability, noise, and signal distortion. Prerequisite: Introductory electromagnetic field theory.
276. Laser Physics. Electromagnetic radiation and its interaction with matter. Topics include Gaussian beams, optical resonators, resonant optical activities, Einstein coefficients, three and four level systems. Gas and solid state lasers, dye lasers, chemical lasers, eximer lasers, gree electron lasers and semiconductor lasers. Modulation, including Q-switching and detection. Prerequisite: EE170L or Physics 182 and EE211 or Physics 211.
281. Random Signal and Noise. Introduction to mathematical methods of describing and analyzing random signals and noise. Review of probability theory; joint, conditional, and marginal distributions; random processes. Time and ensemble averages, correlation, and power spectra. Optimum linear smoothing and predicting filters. Introduction to optimum signal detection, parameter estimation and statistical signal processing. Prerequisites: Mathematics 135 or Statistics 113.
282. Digital Signal Processing. Introduction to the fundamentals of processing signals by digital techniques with applications to practical problems. Discrete time signals and systems, elements of the Z-transform, discrete Fourier transforms, digital filter design techniques, fast Fourier transforms, and discrete random signals.
283. Digital Modulation Techniques. Coding theory. Transmission over bandwidth constrained channels. Signal fading and multipath effects. Spread spectrum. Optical transmission techniques. Prerequisites: EE281 or consent of instructor.
285. Signal Detection and Extraction Theory. Introduction to signal detection and information extraction theory from a statistical decision theory viewpoint. Subject areas covered within the context of a digital environment are decision theory, detection, and estimation of known and random signal in noise, estimation of parameters and adaptive recursive digital filtering, and decision processes with finite memory. Applications to problems in communication theory. Prerequisite: EE281 or consent of instructor.
286. Digital Processing of Speech Signals. Detailed treatment of the theory and application of digital speech processing. Modeling of the speech production system and speech signals; speech processing methods; digital techniques applied in speech transmission, speech synthesis, speech recognition, and speaker verification. Acoustic phonetics, digital speech modeling techniques, LPC analysis methods, speech coding techniques. Application case studies; synthesis, vocoders, DTW (dynamic time warping)/HMM (hidden Markov modeling) recognition methods, speaker verification/identification. Prerequisite: EE282 or equivalent or consent of instructor.
287. Underwater Communications. Elements of communications theory and digital signal processing are combined with basic physics and oceanography to offer an overview of underwater communications, with an emphasis on the radar/sonar problem. Beamforming with transducer arrays. Signal design and target resolution; the ambiguity function. The ocean as a communication channel: sound propagation and ambient noise characteristics. Performance analysis of selected communication scenarios and case studies of operational sonar systems. Prerequisite: Introductory signal processing or consent of instructor.
288. Image and Array Signal Processing. Multidimensional digital signal processing with applications to practical problems in image and sensor array processing. Two-dimensional discrete signals and systems, discrete random fields, 2-D sampling theory, 2-D transforms, image enhancement, image filtering and restoration, space-time signals, beamforming, and inverse problems. Prerequisite: EE282 or consent of instructor.
289. Adaptive Filters. Adaptive digital signal processing with emphasis on the theory and design of finite-impulse response adaptive filters. Stationary discrete-time stochastic processes, Weiner filter theory, the method of steepest descent, adaptive transverse filters using gradient-vector estimation, analysis of the LMS algorithim, least-squares methods, recursive least squares, and least squares lattic adaptive filters. Application examples in noise cancelling, channel equalization, and array processing. (.5 ED/.5 ES) Prerequisites: EE281 and 282 or consent of instructor.
312. Electronic Properties of Submicron Solid-State Devices. Review of quantum mechanics, scattering and transport, Boltzmann transport equation, quantum effects in devices with emphasis on one- and two-dimensional transport, electron-polar sphonon interactions, quantum transport.
316. Advanced Physics of Semiconductor Devices. Semiconductor materials, band structure and carrier statistics. Advanced treatments of metal-semiconductor contacts. Schottky barriers, p-n junctions, bipolar transistors (charge control and Gummel-Poon models), and field-effect transistors (short-channel effects, scaling theory, subthreshold conduction, nonuniformly doped substrates, surface and buried-channel devices, hot-electron effects). Device modeling in two dimensions using PISCES. Prerequisite: EE216.
318. Integrated Circuit Fabrication Laboratory. Introduction to IC fabrication processes. Device layout. Mask design and technology. Wafer cleaning, etching, thermal oxidation, thermal diffusion, lithography, and metalization. Laboratory fabrication and characterization of basic IC elements (p-n junctions, resistors, MOS capacitors, gated diodes, and MOSFETs). Use of four-point probe, ellipsometer, spreading resistance probe, scanning electron microscope, and evaporation system. Testing of basic inverters and gates. Prerequisites: EE218 and consent of instructor.
352. Advanced Topics in Digital Systems. A selection of advanced topics from the areas of digital computer architectures and fault-tolerant computer design. Prerequisite: Introductory computer architecture.
361. Advanced VLSI Design. Theory of advanced VLSI design. Specifications development, methodology, issues, circuit-level trade-offs. Full custom design, standard cell design, gate array design, silicon compilation. Semiconductor technologies and logic families for semi-custom design. Clocking schemes and distribution, race conditions. Design of a variety of circuits (adders, I/O drivers, RAM, FIFO, etc.) Testing of all phases in the lifecycle of an integrated circuit. Top-down design and bottom-up implementation. Student projects. Prerequisite: EE261 or equivalent.
399. Special Readings in Electrical Engineering. Special individual readings in a specified area of study in electrical engineering. Prerequisite: Approval of the Director of Graduate Studies.