Excitonic Devices: Modeling and Applications

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Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Data Science Visualization with Pandas

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Semiconductor Devices II: Defects Engineering

Covers the analysis of measurements and defects engineering in semiconductor devices, including density of states and defect probing.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

Semiconductor Devices II: Contact Resistance Modeling

Explores contact resistance modeling in semiconductor devices, focusing on gate voltage calculation and defect analysis.

MOSFET Basics: NMOS and PMOS Characteristics

Provides an overview of MOSFET operation, focusing on NMOS and PMOS characteristics, including current equations and channel modulation effects.

Common Source Amplifier Exercise

Covers the exercise on determining key parameters of a common source amplifier.

Data Science with Python: Numpy Basics

Introduces the basics of Numpy, a numerical computing library in Python, covering advantages, memory layout, operations, and linear algebra functions.

Building Tomorrow’s Electronics: From Atoms to Devices

Delves into the evolution of transistors, the impact of quantum effects on nano-devices, and the functionality of memristors.