Written, oral, and visual communication skills required by electrical and computer engineers. Prepares students for advanced communication tasks required in academic and professional settings.

This course teaches system-level design, including both software and hardware. Through activities and projects, students gain exposure to entrepreneurship, product lifecycle management, prototyping, and testing.

Introduction to discrete-time signal processing and linear systems. Sampling theorem, filtering, frequency response, Discrete Fourier Transform, Z-Transform. Laboratory emphasizes computer-based signal processing. Credit not allowed for both ECE 2026 and ECE 2025.

This course introduces graduate students to the theory and practice of applying artificial intelligence and machine learning to semiconductor process technology, metrology, manufacturing, and digital twins. The semiconductor industry is undergoing a transformation driven by the increasing complexity of advanced process nodes and the explosion of data generated in modern fabrication facilities. AI/ML techniques and digital twin frameworks are now critical tools for process modeling, equipment monitoring, defect inspection, yield optimization, and accelerating technology development cycles.

Designed for students with a background in semiconductor devices and processes but no prior AI/ML experience, the course begins with a rigorous introduction to machine learning fundamentals using Python, then progressively applies these techniques to real-world semiconductor challenges. Topics include surrogate modeling for TCAD, physics-informed neural networks, virtual metrology, SEM defect classification, yield prediction, fault detection, digital twins for fab modules, and ML-assisted technology development.

The course emphasizes hands-on learning through four Python labs using open semiconductor datasets, two problem-set homeworks, and a substantial semester-long research project. Industry guest lectures provide direct exposure to how these methods are deployed in production fabs and R&D environments.

Advanced digital design issues in the context of VLSI systems. Introduction to a design methodolgy that encompasses the range from architectural models to circuit simulation. Credit not awarded for ECE 3150 and ECE 3060.

Cybersecurity of Drones is an in-depth exploration of security and privacy challenges in cyber-physical systems (CPS), with a primary focus on unmanned aerial vehicles (UAVs). This course equips learners with the foundational knowledge and hands-on skills necessary to analyze, attack, and defend drone systems in real-world scenarios. You will gain expertise in drone architecture, embedded systems security, adversarial machine learning, and CPS resilience, equipping you to understand and mitigate vulnerabilities in UAV operations. Through lectures, research paper discussions, and hands-on labs, you will engage with cutting-edge cybersecurity techniques, including sensor spoofing, actuator manipulation, malware analysis and defensive mechanisms tailored for autonomous aerial systems. By the end of the course, you will not only have a deep technical understanding of drone cybersecurity but also the ability to design resilient and secure UAV architectures, in preparation for careers in cyber-physical security, embedded systems, and critical infrastructure protection.

Introduction to digital signal processing. Sampling theorem, discrete-time Fourier transform. Power spectrum, discrete Fourier transform and the FFT algorithm, Z-transform, digital filter design and implementation.

Fundamental issues associated with autonomous robot control. Emphasizes biological perspective that forms the basis of many current developments in robotics.