This course surveys scientific computing and data science methods relevant for physical electronics. First, traditional numerical analysis methods for the solution of ordinary and partial differential equations will be presented. Next, machine learning approaches and their mathematical basis will be surveyed in view of a modern numerical analysis framework.
This course covers introductory material for semiconductor physics and semiconductor device physics. The course material starts from a discussion of crystal structure and progresses up to p-n junction. More specifically, the course covers the following topics: crystal structure of solids, principles of quantum mechanics, Schrödinger wave equation, energy band theory, statistical mechanics, carriers in semiconductors, extrinsic semiconductor-donors and acceptors, carrier drift, carrier diffusion, carrier generation and recombination, ambipolar transport equation, excess carrier lifetime, p-n junction – equilibrium, p-n junction & applications.
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This course provides students with the knowledge and skills necessary to build a foundation in system programmings for Electrical Engineering, especially focused on operating systems and implementation. Topics include an overview of the components of an OS, concurrency, synchronization, processes, memory management, I/O devices, and file systems.
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Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT
Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT
Copyright ⓒ 2015 KAIST Electrical
Engineering. All rights reserved.
Made by PRESSCAT