Graduate (List)

Quantum information processing exploits the laws of quantum mechanics for computational and communication tasks, and outperforms its classical counterparts. The course is designed to graduate students to introduce quantum information processing. It begins with fundamental principles of quantum information processing and deals with efficient quantum algorithms and communication protocols.

This course deals with various matrix computation algorithms for signal processing such as linear system solving, matrix norm, positive-definite matrix. Toeplitz matrix, orthogonalization/diagonalization, eigenvalue problems, SVD (singular value decomposition), iterative methods for linear systems, and so on.

Quantum computing is a new class of the computing technology that utilizes the principles of quantum physics to represent and process information. This course teaches the fundamental understandings of how quantum computing can outperform digital computing by reviewing the basic principles of quantum computing and its algorithms, and discusses about the practical quantum computing models and their applications.

This course covers propagation of lightwave in isotropic and anisotropic media, Gaussian beams, interaction of matter and light, principles of lasers, modulation, and switching of light, and nonlinear optical phenomena.

This course provides fundamentals on techniques for error-correction or detection. In this course, students study the basics of Finite Field Theory to understand algebraic codes, and based on this, they comprehend cyclic codes, BCH codes, Reed-Solomon codes. In addition, they acquire knowledge about channel codes defined on graphs, such as Convolutional codes, Turbo codes, Low-density Parity-check (LDPC) codes. This course also provides a short introduction to signal space codes.

This course covers fundamental VLSI device physics for graduate students. After a brief review of basic quantum mechanics and semiconductor processes, the lecturer will cover basic principles of operation in semiconductor devices including PN junction, MOS Capacitor, MOSFET and bipolar transistors with a strong emphasis on deep submicron secondary effects of MOSFET and bipolar transistors for extensive understanding of advanced device engineering. (Prerequisite: EE362)

In this course, the technology trend of the next generation information display devices will be introduced and their basic principles will be studied. In particular, LCD, PDP, OLED, and FED are mainly discussed.

This course primarily emphasizes “quantum mechanics” and “statistical physics” for engineers. Quantum mechanics includes a history of quantum physics, Schrödinger equation, a concept of a wavepacket, and N-degrees of freedom. Statistical physics covers a motivation, concept of ensemble average, Boltzmann distribution, Bose-Einstein distribution, Fermi-Dirac distribution, and Non-Equilibrium statistics.

In this course, we will discover microelectromechanical systems (MEMS) in electrical engineering perspective, touching a complete set of design, fabrication, and applications. With respect to designing MEMS, we will explore various working principles, CAD tools including semiconductor design tools, and signal processing circuits. Also, core semiconductor processing technologies and a wide range of micro-machining techniques are studied in depth, in order to fabricate MEMS. We will address important issues in major fields of MEMS applications, including microsensors, RF/microwave, optical, and bio / microfluidic MEMS, especially in an electrical engineering viewpoint.

In this course, various photovoltaic devices and systems are introduced. This course deals with the basic theory of solar cells, the structures and characteristics of various solar cells, and the recent R&D trend and future prospects of photovoltaic technologies. (Prerequisites: EE211)