Graduate (List)

Academics

Graduate Program

Curriculum

Circuit
EE.89914(021)

At first, we study the electrical characteristics and related neuro-dynamics, modelling, and CMOS circuit implementation methods for a single neuron. And then, we extend from single to neural network such as spike neural network (SNN). This course also introduces how to apply SNN architecture to semiconductor devices and circuits with specific case studies.

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Prerequisite

This course introduces a basic concept, definition and trends of neuromorphic and processing-in-memory (PIM). In addition, this course covers the hardware implementation of neuromorphic and PIM. Also, real implementation results of state-of-the-art neuromorphic and PIM hardware will be studied.

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Prerequisite

This course covers the practical design of power converter topology. Based on duality principles, topological study of Non-isolation converter and isolated converter are carried out. The various topology extension of buck, boost and buck-boost topologies and forward, half bridge, push-pull and full bridge are investigated.

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Prerequisite

The relationship between intelligence and information is explained. New realization techniques of intelligent systems are illustrated. Design methods of intelligent systems are explained in relation to information flow.

Recommend

Prerequisite

EE.91200

A research course for non-thesis Master’s degree program student. Research subject can be selected with a professor who is going to guide research during the semester.

Recommend

Prerequisite

EE.92200

In this course, the student selects an advisor and a research topic, and conducts research for basic understanding and application of a specific topic in electrical engineering.

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Prerequisite

EE.93100

This course is composed of invited lectures given by experts in electrical engineering and various related subject areas.

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Prerequisite