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Electrical and Computer Engineering
Professor Collins, Chair; Associate Professor Board, Associate Chair; Associate Professor Cummer, Director of Graduate
Studies (3455 CIEMAS); Professors Brady, Brown, Carin, Chakrabarty, Fair, Glass, Joines, Jokerst, Katsouleas, Krolik,
Liu, Massoud, Nolte, Smith, Trivedi; Associate Professors Board, Brooke, Cummer, Kedem, Nowacek, Sorin, Teitsworth;
Assistant Professors Dwyer, George, Lebeck, Kim, Reynolds, Roy Choudhury, Stiff-Roberts, Willett, Yoshie; Professors
Emeriti Casey, Marinos, Owen, Wang and Wilson; Professor of the Practice Ybarra; Associate Professor of the Practice
Huettel; Assistant Professor of the Practice Gustafson; Assistant Research Professors Degiron, Liao, Morizio, Remus,
Tantum, Wolter; Adjunct Professors Derby and Lampert; Adjunct Associate Professor Janet, Ozev, Pitsianis, Vu; Visiting
Professors Kaiser and McCumber
     Graduate study in the Department of Electrical and Computer Engineering (ECE) is intended to prepare
students for leadership roles in academia, industry, and government that require creative technical problem solving
skills. The department offers both PhD and MS degree programs with options for study in a broad spectrum of areas
within electrical and computer engineering. Research and course offerings in the department are organized into five
areas of specialization: computer engineering, sensing and waves, micro/nano systems, photonics, and signal
processing and communications. Detailed descriptions of course offerings, faculty research interests, and degree
requirements may be found on the department's Web Site, http://www.ece.duke.edu/. Interdisciplinary programs are
also available that connect the above areas with those in other engineering departments, computer science, the
natural sciences, and the Medical School. Students in the department may also be involved in research conducted in
one of Duke's Centers (e.g. the Fitzpatrick Institute for Photonics and Communications). Recommended
prerequisites for graduate study in electrical engineering include knowledge of basic mathematics, statistics, and
physics, electrical networks, electromagnetics, and system theory. Students with non-electrical and/or computer
engineering undergraduate degrees are welcome to apply but should discuss their enrollment and course requirement
options with the Director of Graduate Studies. The MS degree program includes thesis, project, or courses-only
options. A qualifying examination is required for the PhD degree program and must be taken by the beginning of the
third semester of enrollment. The exam is intended to assess the student's potential for success as a researcher in
their chosen sub-discipline. To ensure breadth of study, PhD students are required to take at least three courses in
two areas outside their area of specialization. There is no foreign language requirement.
Electrical and Computer Engineering (ECE)
211. Quantum Mechanics. Discussion of wave mechanics including elementary applications, free particle
dynamics, Schrödinger 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: Mathematics 107 or equivalent. Instructor: Brady, Brown, or Stiff-Roberts. 3
212. Introduction to Micro-Electromechanical Systems (MEMS). Design, simulation, fabrication, and
characterization of micro-electromechanical systems (MEMS) devices. Integration of non-conventional devices into
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functional systems. Principles of fabrication, mechanics in micrometer scale, transducers and actuators, and issues in
system design and integration. Topics presented in the context of example systems. Lab covers design, simulation,
and realization of MEMS devices using commercially available foundry process. Prerequisite: ECE 51L or ME
125L or equivalent. Instructor: Kim.. 3 units.
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 of metals, energy bands, and
superconductivity, with emphasis on understanding electrical and optical properties of solids. Prerequisite: quantum
physics at the level of Physics 143L or Electrical and Computer Engineering 211. Instructor: Teitsworth. 3 units.
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: Electrical
and Computer Engineering 211 or consent of instructor. Instructor: Staff. 3 units.
216. Semiconductor Devices for Integrated Circuits. Basic semiconductor properties (energy-band structure,
effective density of states, effective masses, carrier statistics, and carrier concentrations). Electron and hole behavior
in semiconductors (generation, recombination, drift, diffusion, tunneling, and basic semiconductor equations).
Current-voltage, capacitance-voltage, and static and dynamic models of PN Junctions, Schottky barriers,
Metal/Semiconductor Contacts, Bipolar-Junction Transistors, MOS Capacitors, MOS-Gated Diodes, and MOS
Field-Effect Transistors. SPICE models and model parameters. Prerequisites: ECE 162. Instructor: Massoud. 3 units.
217. Analog Integrated Circuits. Analysis and design of bipolar and CMOS analog integrated circuits. SPICE
device models and circuit macromodels. Classical operational amplifier structures, current feedback amplifiers, and
building blocks for analog signal processing, including operational transconductance amplifiers and current
conveyors. Biasing issues, gain and bandwidth, compensation, and noise. Influence of technology and device
structure on circuit performance. Extensive use of industry-standard CAD tools, such as Analog Workbench.
Prerequisite: Electrical Engineering 216. Instructor: Staff. 3 units.
218. Integrated Circuit Engineering. Basic processing techniques and layout technology for integrated circuits.
Photolithography, diffusion, oxidation, ion implantation, and metallization. Design, fabrication, and testing of
integrated circuits. Prerequisite: Electrical and Computer Engineering 162 or 163L. Instructor: Fair. 3 units.
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: Electrical and Computer Engineering 162
and 163L. Instructor: Massoud. 3 units.
225. Nanophotonics. Theory and applications of nanophotonics and sub-wavelength optics. Photonic crystals, near-
field optics, surface-plasmon optics, microcavities, and nanoscale light emitters. Prerequisite: Electrical and
Computer Engineering 53L or equivalent. Instructor: Yoshie. 3 units.
227. Quantum Information Science. Fundamental concepts and progress in quantum information science.
Quantum circuits, quantum universality theorem, quantum algorithms, quantum operations and quantum error
correction codes, fault-tolerant architectures, security in quantum communications, quantum key distribution,
physical systems for realizing quantum logic, quantum repeaters and long-distance quantum communication.
Prerequisites: ECE 211 or Physics 211 or equivalent. Instructor: Kim. 3 units. C-L: Physics 272
226. Optoelectronic Devices. Devices for conversion of electrons to photons and photons to electrons. Optical
processes in semiconductors: absorption, spontaneous emission and stimulated emission. Light-emitting diodes
(LEDs), semiconductor lasers, quantum-well emitters, photodetectors, modulators and optical fiber networks.
Prerequisite: Electrical and Computer Engineering 216 or equivalent. Instructor: Stiff-Roberts. 3 units.
241. Linear System Theory and Optimal Control. Consideration of system theory fundamentals; observability,
controllability, and realizability; stability analysis; linear feedback, linear quadratic regulators, Riccati equation, and
trajectory tracking. Prerequisite: Electrical and Computer Engineering 141. Instructor: P. Wang. 3 units.
243. Pattern Classification and Recognition Technology. Theory and practice of recognition technology: pattern
classification, pattern recognition, automatic computer decision-making algorithms. Applications covered include
medical diseases, severe weather, industrial parts, biometrics, bioinformation, animal behavior patterns, image
processing, and human visual systems. Perception as an integral component of intelligent systems. This course
prepares students for advanced study of data fusion, data mining, knowledge base construction, problem-solving
methodologies of "intelligent agents" and the design of intelligent control systems. Prerequisites: Mathematics 107,
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Statistics 113 or Mathematics 135, Computer Science 6, or consent of instructor. Instructor: Collins or P. Wang. 3
245. Digital Control Systems. Review of traditional techniques used for the design of discrete-time control
systems; 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. Consent of instructor
required. Instructor: Staff. 3 units.
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. This course is
designed to satisfy the need of several engineering disciplines. Prerequisite: Electrical and Computer Engineering
141 or equivalent. Instructor: Staff. 3 units. C-L: Mechanical Engineering and Materials Science 232
250. Computer Networks and Distributed Systems. 3 units. C-L: see Computer Science 214
251. Advanced Digital System Design. This course covers the fundamentals of advanced digital system design, and
the use of a hardware description language, VHDL, for their synthesis and simulation. Examples of systems
considered include the arithmetic/logic unit, memory, and microcontrollers. The course includes an appropriate
capstone design project that incorporates engineering standards and realistic constraints in the outcome of the design
process. Additionally, the designer must consider most of the following: Cost, environmental impact,
manufacturability, health and safety, ethics, social and political impact. Each design project is executed by a team of
4 or 5 students who are responsible for generating a final written project report and making an appropriate
presentation of their results to the class. Prerequisite: Electrical and Computer Engineering 52L and Senior/graduate
student standing. Instructor: Derby. 3 units.
252. Advanced Computer Architecture I. 3 units. C-L: see Computer Science 220
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: Computer Science 110; Electrical and
Computer Engineering 152. Instructor: Staff. 3 units.
254. Fault-Tolerant and Testable Computer Systems. Technological reasons for faults, fault models, information
redundancy, spatial redundancy, backward and forward error recovery, fault-tolerant hardware and software,
modeling and analysis, testing, and design for test. Prerequisite: Electrical and Computer Engineering 152 or
equivalent. Instructor: Sorin. 3 units. C-L: Computer Science 225
255. Probability for Electrical and Computer Engineers. Basic concepts and techniques used stochastic modeling
of systems with applications to performance and reliability of computer and communications system. Elements of
probability, random variables (discrete and continuous), expectation, conditional distributions, stochastic processes,
discrete and continuous time Markov chains, introduction to queuing systems and networks. Prerequisite:
Mathematics 107. Instructor: Trivedi. 3 units. C-L: Computer Science 226
256. Wireless Networking and Mobile Computing. Theory, design, and implementation of mobile wireless
networking systems. Fundamentals of wireless networking and key research challenges. Students review pertinent
journal papers. Significant, semester-long research project. Networking protocols (Physical and MAC, multi-hop
routing, wireless TCP, applications), mobility management, security, and sensor networking. Prerequisites:
Electrical and Computer Engineering 156 or Computer Science 114. Instructor: Roy Choudhury. 3 units. C-L:
Computer Science 215
257. Performance and Reliability of Computer Networks. Methods for 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: Electrical and Computer Engineering 156 and 255. Instructor: Trivedi. 3 units.
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 McCulloch and Pitts model. 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 combinatorial
optimization. Instructor: Staff. 3 units.
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259. Advanced Computer Architecture II. 3 units. C-L: see Computer Science 221
261. CMOS VLSI Design Methodologies. Emphasis on full-custom chip design. Extensive use of CAD tools for
IC design, simulation, and layout verification. Techniques for designing high-speed, low-power, and easily-testable
circuits. Semester design project: Groups of four students design and simulate a simple custom IC using Mentor
Graphics CAD tools. Teams and project scope are multidisciplinary; each team includes students with interests in
several of the following areas: analog design, digital design, computer science, computer engineering, signal
processing, biomedical engineering, electronics, photonics. A formal project proposal, a written project report, and a
formal project presentation are also required. The chip design incorporates considerations such as cost, economic
viability, environmental impact, ethical issues, manufacturability, and social and political impact. Prerequisites:
Electrical and Computer Engineering 52L and Electrical and Computer Engineering 163L. Some background in
computer organization is helpful but not required. Instructor: Chakrabarty. 3 units.
262. Analog Integrated Circuit Design. Design and layout of CMOS analog integrated circuits. Qualitative review
of the theory of pn junctions, bipolar and MOS devices, and large and small signal models. Emphasis on MOS
technology. Continuous time operational amplifiers. Frequency response, stability and compensation. Complex
analog subsystems including phase-locked loops, A/D and D/A converters, switched capacitor simulation, layout,
extraction, verification, and MATLAB modeling. Projects make extensive use of full custom VLSI CAD software.
Prerequisite: Electrical and Computer Engineering 162 or 163L. Instructor: Morizio. 3 units.
263. Multivariable Control. 3 units. C-L: Civil Engineering 263, Mechanical Engineering and Materials Science
264. CAD For Mixed-Signal Circuits. The course focuses on various aspects of design automation for mixed-
signal circuits. Circuit simulation methods including graph-based circuit representation, automated derivation and
solving of nodal equations, and DC analysis, test automation approaches including test equipments, test generation,
fault simulation, and built-in-self-test, and automated circuit synthesis including architecture generation, circuit
synthesis, tack generation, placement and routing are the major topics. The course will have one major project, 4-6
homework assignments, one midterm, and one final. Prerequisites: ECE 163L. Permission of instructor required.
Instructor: Staff. 3 units.
266. Synthesis and Verification of VLSI Systems. Algorithms and CAD tools for VLSI synthesis and design
verification, logic synthesis, multi-level logic optimization, high-level synthesis, logic simulation, timing analysis,
formal verification. Prerequisite: Electrical and Computer Engineering 52L or equivalent. Instructor: Chakrabarty. 3
267. Radiofrequency (RF) Transceiver Design. Design of wireless radiofrequency transceivers. Analog and digital
modulation, digital modulation schemes, system level design for receiver and transmitter path, wireless
communication standards and determining system parameters for standard compliance, fundamentals of synthesizer
design, and circuit level design of low-noise amplifiers and mixers. Prerequisites: Electrical and Computer
Engineering 54L and Electrical and Computer Engineering 163L or equivalent. Instructor: Staff. 3 units.
269. VLSI System Testing. Fault modeling, fault simulation, test generation algorithms, testability measures,
design for testability, scan design, built-in self-test, system-on-a-chip testing, memory testing. Prerequisite:
Electrical and Computer Engineering 52L or equivalent. Instructor: Chakrabarty. 3 units.
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: Electrical and Computer Engineering 53L. Instructor: Carin, Joines,
Liu, or Smith. 3 units.
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: Electrical and Computer Engineering 53L or 271. Instructor: Joines. 3 units.
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:
Electrical and Computer Engineering 53L or equivalent. Instructor: Joines. 3 units.
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
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oscillators. Topics on stability, noise, and signal distortion. Prerequisite: Electrical and Computer Engineering 53L
or equivalent. Instructor: Joines. 3 units.
277. Computational Electromagnetics. Systematic discussion of useful numerical methods in computational
electromagnetics including integral equation techniques and differential equation techniques, both in the frequency
and time domains. Hands-on experience with numerical techniques, including the method of moments, finite
element and finite-difference time-domain methods, and modern high order and spectral domain methods.
Prerequisite: Electrical and Computer Engineering 271 or consent of instructor. Instructor: Carin or Liu. 3 units.
278. Inverse Problems in Electromagnetics and Acoustics. Systematic discussion of practical inverse problems in
electromagnetics and acoustics. Hands-on experience with numerical solution of inverse problems, both linear and
nonlinear in nature. Comprehensive study includes: discrete linear and nonlinear inverse methods, origin and
solution of nonuniqueness, tomography, wave-equation based linear inverse methods, and nonlinear inverse
scattering methods. Assignments are project oriented using MATLAB. Prerequisites: Graduate level acoustics or
electromagnetics (Electrical and Computer Engineering 271), or consent of instructor. Instructor: Liu. 3 units.
279. Waves in Matter. Analysis of wave phenomena that occur in materials based on fundamental formulations for
electromagnetic and elastic waves. Examples from these and other classes of waves are used to demonstrate general
wave phenomena such as dispersion, anisotropy, and causality; phase, group, and energy propagation velocities and
directions; propagation and excitation of surface waves; propagation in inhomogeneous media; and nonlinearity and
instability. Applications that exploit these wave phenomena in general sensing applications are explored.
Prerequisites: Electrical and Computer Engineering 53L. Instructor: Cummer. 3 units.
281. Random Signals and Noise. Introduction to mathematical methods of describing and analyzing random
signals and noise. Review of basic 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.
Prerequisite: Mathematics 135 or Statistics 113. Instructor: Collins or Nolte. 3 units.
282. Digital Signal Processing. Introduction to fundamental algorithms used to process digital signals. Basic
discrete time system theory, the discrete Fourier transform, the FFT algorithm, linear filtering using the FFT, linear
production and the Wierner filter, adaptive filters and applications, the LMS algorithm and its convergence,
recursive least-squares filters, nonparametric and parametric power spectrum estimation minimum variance and
eigenanalysis algorithms for spectrum estimation. Prerequisite: Electrical and Computer Engineering 281 or
equivalent with consent of the instructor. Instructor: Collins, Krolik, Nolte, or Willett. One course. 3 units.
283. Digital Communication Systems. Digital modulation techniques. Coding theory. Transmission over
bandwidth constrained channels. Signal fading and multipath effects. Spread spectrum. Optical transmission
techniques. Prerequisite: Electrical and Computer Engineering 281 or consent of instructor. Instructor: Staff. 3 units.
284. Acoustics and Hearing (GE, IM). 3 units. C-L: see Biomedical Engineering 235
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 signals in noise, estimation of parameters and
adaptive recursive digital filtering, and decision processes with finite memory. Applications to problems in
communication theory. Prerequisite: Electrical and Computer Engineering 281 or consent of instructor. Instructor:
Nolte. 3 units.
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: Electrical and Computer Engineering 182 or equivalent or consent
of instructor. Instructor: Staff. 3 units.
287. Information Theory. This class provides an introduction to information theory. The student is introduced to
entropy, mutual information, relative entropy and differential entropy, and these topics are connected to practical
problems in communications, compression, and inference. The class is appropriate for beginning graduate students
who have a good background in undergraduate electrical engineering, computer science or math. Instructor: Carin. 3
288. Sensor Array Signal Processing. An in-depth treatment of the fundamental concepts, theory, and practice of
sensor array processing of signals carried by propagating waves. Topics include: multidimensional frequency-
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domain representations of space-time signals and linear systems; apertures and sampling of space-time signals;
beamforming and filtering in the space-time and frequency domains, discrete random fields; adaptive beamforming
methods; high resolution spatial spectral estimation; optimal detection, estimation, and performance bounds for
sensor arrays; wave propagation models used in sensor array processing; blind beamforming and source separation
methods; multiple-input-multiple-output (MIMO) array processing; application examples from radar, sonar, and
communications systems. Instructor: Krolik. 3 units.
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, Wiener filter theory, the method of steepest
descent, adaptive transverse filters using gradient-vector estimation, analysis of the LMS algorithm, least-squares
methods, recursive least squares and least squares lattic adaptive filters. Application examples in noise canceling,
channel equalization, and array processing. Prerequisites: Electrical and Computer Engineering 281 and 282 or
consent of instructor. Instructor: Krolik. 3 units.
298. Advanced Topics in Electrical and Computer Engineering. Opportunity for study of advanced subjects in
electrical and computer engineering. Instructor: Staff. 1 unit.
299. Advanced Topics in Electrical and Computer Engineering. Opportunity for study of advanced subjects
related to programs within the electrical and computer engineering department tailored to fit the requirements of a
small group. Instructor: Staff. 3 units.
For Graduate Students Only
310. Foundations of Nanoscale Science and Technology. This course is the introductory course for the Graduate
Certificate Program in Nanoscience (GPNANO) and is designed to introduce students to the interdisciplinary
aspects of nanoscience by integrating important components of the broad research field together. This integrated
approach will cross the traditional disciplines of biology, chemistry, electrical & computer engineering, computer
science, and physics. Fundamental properties of materials at the nanoscale, synthesis of nanoparticles,
characterization tools, and self-assembly. Prerequisites: Physics 62L and Chem 21L or instructor approval. C-L:
NANO 200 pending in COMPSCI, CHEM, and PHYS. Instructor: Dwyer. 3 units. C-L: Nanosciences 310
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: Electrical Engineering 216. Instructor: Massoud. 3 units.
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 metallization.
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. Prerequisite: Electrical Engineering 218
and consent of instructor. Instructor: Massoud. 3 units.
322. Quantum Electronics. Quantum theory of light-matter interaction. Laser physics (electron oscillator model, rate
equations, gain, lasing condition, oscillation dynamics, modulation) and nonlinear optics (electro-optic effect,
second harmonic generation, phase matching, optical parametric oscillation and amplification, third-order
nonlinearity, optical bistability.) Prerequisite ECE 211, Physics 211, or equivalent. Instructors: Stiff-Roberts or
Yoshie. One course. 3 units.
352. Advanced Topics in Digital Systems. A selection of advanced topics from the areas of digital computer
architectures and fault-tolerant computer design. Prerequisite: Electrical Engineering 252 or equivalent. Instructor:
Staff. 3 units. C-L: Computer Science 320
361. Advanced VLSI Design. 3 units. C-L: see Computer Science 322
371. Advanced Electromagnetic Theory. Instructor: Staff. 3 units.
373. Selected Topics in Field Theory. Instructor: Staff. 3 units.
375. Optical Imaging and Spectroscopy. Wave and coherence models for propagation and optical system analysis.
Fourier optics and sampling theory. Focal plane arrays. Generalized and compressive sampling. Impulse response,
modulation transfer function and instrument function analysis of imaging and spectroscopy. Code design for optical
measurement. Dispersive and interferometric spectroscopy and spectral imaging. Performance metrics in optical
imagine systems. Prerequisite: Electrical and Computer Engineering 53L and 54L. Instructor: Brady. 3 units.
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376. Lens Design. Paraxial and computational ray tracing. Merit functions. Wave and chromatic aberrations. Lenses
in photography, microscopy and telescopy. Spectrograph design. Emerging trends in lens system design, including
multiple aperture and catadioptric designs and nonimaging design for solar energy collection. Design project
management. Each student must propose and complete a design study, including a written project report and a
formal design review. Prerequisite: Electrical and Computer Engineering 122 or 274. Instructor: Brady. 3 units. 3
391. Internship. Student gains practical electrical and computer engineering experience by taking a job in industry,
and writing a report about this experience. Requires prior consent from the student's advisor and from the director of
graduate studies. May be repeated with consent of the advisor and the director of graduate studies. Credit/no credit
grading only. Instructor: Staff. 1 unit.
399. Special Readings in Electrical Engineering. Special individual readings in a specified area of study in
electrical engineering. Approval of director of graduate studies required. 1 to 4 units. Instructor: Graduate staff.
Variable credit.

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