Explores measurement and behavior of high-frequency circuits and components. Equivalent circuit models for lumped elements. Measurement of standing waves, power, and frequency. Use of vector network analyzers and spectrum analyzers. Computer-aided design, fabrication, and characterization of microstrip circuits. Corequisite: ECE 5260 or instructor permission.

This course explores the intricacies of AI hardware, including the current landscape and anticipating the necessary developments in response to AI's rapid growth and widespread integration across all computing tiers. Through this exploration, you will gain an understanding of both the existing technologies and the future challenges in AI hardware design and implementation.

Focuses on techniques for designing and analyzing dependable computer-based systems. Topics include basic dependability concepts and attributes, fault models and effects, combinatorial and state-space modeling, hardware redundancy, error detecting and correcting codes, time redundancy, software fault tolerance, checkpointing and recovery, reliable networked systems, error detection techniques, and experimental dependability evaluation techniques.Prerequisites: A basic knowledge of probability and computer architecture is required. A working knowledge of programming is required for homework and mini projects.

Integration of computer organization concepts such as data flow, instruction interpretation, memory systems, interfacing, and microprogramming with practical and systematic digital design methods such as behavioral versus structural descriptions, divide-and-conquer, hierarchical conceptual levels, trade-offs, iteration, and postponement of detail.  Design exercises are accomplished using a hardware description language and simulation.  Prerequisite by topic:  Digital Logic Design (ECE 2330 or equivalent), Introductory Computer Architecture (ECE 3330 or equivalent), Assembly Language Programming.

Interactions between robots and humans are influenced by form, function and expectations. Quantitative techniques evaluate performance of specific tasks and functions. Qualitative techniques are used to evaluate the interaction and to understand expectations and perceptions of the human side of the interaction. Students use humanoid robots to develop and evaluate interactions within a specific application context.

A first-level graduate course covering a topic not normally covered in the graduate course offerings. The topic will usually reflect new developments in the electrical and computer engineering field. Offering is based on student and faculty interests. Prerequisite: Instructor permission.

This one-hour weekly seminar course features presentations given by ECE faculty members, to introduce various research areas, topics, and advances in Electrical and Computer Engineering. This course is required for all first-year ECE graduate students. 

Optoelectronics merges optics and microelectronics. Optoelectronic devices and circuits have become core technologies for several key technical areas such as telecommunications, information processing, optical storage, and sensors. This course will cover devices that generate (semiconductor light emitting diodes and lasers), modulate, amplify, switch, and detect optical signals. Also included are solar cells, photonic crystals, and plasmonics.

Design and analysis of analog integrated circuits. Topics include feedback amplifier analysis and design including stability, compensation, and offset-correction; layout and floor-planning issues associated with mixed-signal IC design; selected applications of analog circuits such as A/D and D/A converters, references, and comparators; and extensive use of CAD tools for design entry, simulation, and layout. Includes an analog integrated circuit design project. Prerequisite: ECE 3103 and 3632, or equivalent.

This course introduces students to the key concepts in convex optimization theory with the goal of enabling them to formulate and solve various convex optimization problems arising in engineering, data science, and machine learning. Non-convex optimization techniques in deep learning will also be introduced.