Electrical Engineering, BSE
Electrical engineering connects the physical world with the information world. Electrical engineers apply physics and chemistry in modern nanotechnology devices, encode and manipulate information in circuits and networks, and mathematically understand and reason with large amounts of data in real time. This makes electrical engineering one of the broadest forms of engineering, resulting in a multitude of possible careers. The societal impact of electrical engineering can be found in numerous domains, from smartphones, 5G wireless, and medical imaging to electric/driverless cars and the Internet of Things. Electrical engineering includes the engineering of electrons, magnets, photons, electro-magnetic waves, quantum states, and electro-mechanical structures. Electrically engineering systems provide communication, sensing, actuation, display, storage, conversion, control, and computation. The electrical engineering discipline includes both the design and implementation of physical realizations (devices, circuits, antennas) and the mathematical tools for optimizing the exploitation of these systems (control theory, information theory, digital logic, signal processing).
For more information: https://www.seas.upenn.edu/prospective-students/undergrad/majors/electrical-engineering/
Electrical Engineering (EE) Major Requirements
37 course units are required.
| Code | Title | Course Units |
|---|---|---|
| Engineering | ||
| CIS 1100 | Introduction to Computer Programming | 1 |
| ESE 1110 | Atoms, Bits, Circuits and Systems 1 | 1 |
| CIS 1200 | Programming Languages and Techniques I | 1 |
| or CIS 2400 | Introduction to Computer Systems | |
| ESE 2150 | Electrical Circuits and Systems | 1.5 |
| ESE 2180 | Electronic, Photonic, and Electromechanical Devices | 1.5 |
| ESE 2240 | Signal and Information Processing | 1.5 |
| Intermediate or Advanced ESE Elective | 1 | |
Intermediate ESE courses are considered 2000 level and above | ||
| Advanced ESE courses | ||
| Choose four advanced electives from the following lists: | 4-4.5 | |
| Circuits and Computer Engineering: | ||
| Fundamentals of Solid-State Circuits | ||
| Medical Devices Laboratory | ||
| Embedded Systems/Microcontroller Laboratory | ||
| TinyML: Tiny Machine Learning for Embedded Systems | ||
| Circuit-Level Modeling, Design, and Optimization for Digital Systems | ||
| Analog Integrated Circuits | ||
| Internet of Things Sensors and Systems | ||
| IoT Edge Computing | ||
| Smart Devices | ||
| System-on-a-Chip Architecture | ||
| Hardware/Software Co-Design for Machine Learning | ||
| RFIC (Radio Frequency Integrated Circuit) Design | ||
| Power Electronics | ||
| Datacenter Architecture | ||
| Integrated Communication Systems | ||
| Nanodevices and Nanosystems: | ||
| Principles of Optics and Photonics | ||
| Nanofabrication of Electrical Devices | ||
| Quantum Circuits and Systems | ||
| Electromagnetic and Optics | ||
| The Physics of Solid State Energy Devices | ||
| Quantum Engineering | ||
| Nanophotonics: Light at the Nanoscale | ||
| Nanoelectronics | ||
| Nanorobotics | ||
| Integrated Photonic Systems | ||
| Information and Decision Systems: | ||
| Stochastic Systems Analysis and Simulation | ||
| Foundations of Data Science | ||
| Fourier Analysis and Applications in Engineering, Mathematics, and the Sciences | ||
| Introduction to Networks and Protocols | ||
| Linear Systems Theory | ||
| Networking - Theory and Fundamentals | ||
| Feedback Control Design and Analysis | ||
| Introduction to Optimization Theory | ||
| Dynamical Systems for Engineering and Biological Applications | ||
| Graph Neural Networks | ||
| Estimation and Detection Theory | ||
| Digital Signal Processing | ||
| Data Mining: Learning from Massive Datasets | ||
| Principles of Deep Learning | ||
| Transportation Planning Methods | ||
| Advance Transportation Seminar | ||
| Risk Analysis and Environmental Management | ||
| Modern Convex Optimization | ||
| Combinatorial Optimization | ||
| F1/10 Autonomous Racing Cars | ||
| Learning for Dynamics and Control | ||
| Model Predictive Control | ||
| Learning in Robotics | ||
| Information Theory | ||
| One of the Advanced Electives may be an Advanced ESE elective, BE 5210 or CIS 4710 or CIS 5200 | ||
| Design and Project Courses 2 | ||
| ESE 2900 & ESE 2910 | Introduction to Electrical and Systems Engineering Research Methodology and Introduction to Electrical and Systems Engineering Research and Design | 1.5 |
| or ESE 3190 | Fundamentals of Solid-State Circuits | |
| or ESE 3360 | Nanofabrication of Electrical Devices | |
| or ESE 3500 | Embedded Systems/Microcontroller Laboratory | |
| or ESE 4210 | Control For Autonomous Robots | |
| or BE 4700 | Medical Devices | |
| ESE 4500 | Senior Design Project I - EE and SSE | 1 |
| ESE 4510 | Senior Design Project II - EE and SSE | 1 |
| Math and Natural Science | ||
| MATH 1400 | Calculus, Part I | 1 |
| MATH 1410 | Calculus, Part II | 1 |
| MATH 2400 | Calculus, Part III | 1 |
| ESE 3010 | Engineering Probability | 1 |
| MEAM 1100 | Introduction to Mechanics | 1 |
| or PHYS 0140 | Principles of Physics I (without laboratory) | |
| or PHYS 0150 | Principles of Physics I: Mechanics and Wave Motion | |
| or PHYS 0170 | Honors Physics I: Mechanics and Wave Motion | |
| ESE 1120 | Engineering Electromagnetics (students passing the ESE E&M review module may substitute an ESE approved E&M course) | 1.5 |
| CHEM 1012 | General Chemistry I | 1 |
| or EAS 0091 | Chemistry Advanced Placement/International Baccalaureate Credit (Engineering Students Only) | |
| or BIOL 1101 | Introduction to Biology A | |
| or BIOL 1121 | Introduction to Biology - The Molecular Biology of Life | |
| Math Elective | 1 | |
| Math or Natural Science Elective | 1 | |
| Natural Science Lab (if applicable) 3 | .5 | |
| Professional Electives 4 | ||
| Math, Science, or Engineering Electives | 3 | |
| Professional Elective - Select from the following: | 1 | |
Math, Science, or Engineering Elective | ||
| Engineering Economics | ||
| Engineering Entrepreneurship I | ||
| Foundations of Leadership | ||
| Management of Technology | ||
| Scaling Operations in Technology Ventures: Linking Strategy and Execution | ||
| General Electives 5 | ||
| EAS 2030 | Engineering Ethics (or equivalent) | 1 |
| or LAWM 5060 | ML: Technology Law | |
| Select 4 Social Science or Humanities courses | 4 | |
| Select 2 Social Science or Humanities or Technology in Business & Society courses | 2 | |
| Total Course Units | 37 | |
- 1
If not taken freshman year, must be replaced by another department approved engineering course.
- 2
If BE 4700 is taken, an additional .5 CU engineering credit is required
- 3
If BIOL 1121, CHEM 1012, EAS 0091, MEAM 1100 or PHYS 0140 are taken, choose one natural science lab from the list: BIOL 1124 Introductory Organismal Biology Lab, PHYS 0050 Physics Laboratory I, MEAM 1470 Introduction to Mechanics Lab, CHEM 1101 General Chemistry Laboratory I or another department approved Natural Science lab.
- 4
At most, two freshman-level engineering courses may be used as a Professional Elective
- 5
Must include a Writing Seminar (a list of approved Writing Seminars can be found in the SEAS Undergraduate Handbook)
Concentrations
Students may select one of six concentrations:
- Data Science
- Microsystems and Nanotechnology
- Mixed-Signal and RF Integrated Circuits
- Photonics and Quantum
- Robotics
- System-on-A-Chip Design
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2023 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.