Designed to Inspire
This department balances technical rigor and curricular flexibility to provide a worldclass engineering education. We want to inspire our students, and we want our students to inspire their peers.
What to Expect
The program begins in the sophomore year with a set of unifying foundation courses (information, circuits, devices and digital logic) that cover the breadth of the discipline and prepare students for advanced electives.
Students build on this foundation with the introduction of systems and their design, followed by a set of departmental electives in a concentration area. Within these concentrations, students see the interaction of theory and application and have a choice on which aspects to focus.
Students tailor their areas of concentration in consultation with a faculty adviser. Possible areas of concentration include Data and Information, Security and Privacy, Computer Systems, Energy and Environment, Quantum Computing and Applied Physics, to name only a few. For a complete list, download the undergraduate student handbook.
Many students also pursue an interdisciplinary certificate from one of the many programs offered at Princeton. This is the equivalent of a "minor", and is optional, not required.
Graduates from this department earn a Bachelor of Science in Engineering (BSE).
Where to Start
To be adequately prepared for the firstyear engineering program at Princeton, students should take highschool mathematics through calculus (if possible), as well as highschool physics and chemistry. Many students enter Princeton with advanced placement in one or more of these subjects, but this is not a requirement for admission or for success in the program.
Thirtysix courses are required for the fouryear program. Students granted advanced standing participate in a threeyear program and must complete 28 courses for the BSE (this is rare).
Questions may be directed to the Undergraduate Program Coordinator or the faculty Department Representative.
Undergraduate Contacts
Concentration
Each ECE major chooses an area of concentration within the field, which gives coherent shape to your classes over time. We have 10 suggested areas with prescribed course lists.


Required:
ECE 304 Electronic Circuits: Devices to ICs (S)
Two courses from:
ECE 445 SolidState Electronic Devices (F)
ECE 368 Intro to Wireless Communication Systems (S)
COS/ECE 375 Computer Architecture and Organization (S)
ECE 382 Probabilistic Systems and Information Processing (S)
ECE 462 Design of VLSI (F)
ECE 464 Embedded Computing (S)
ECE 472 Secure Computers (S)
ECE 475 Computer Architecture (S)
ECE 481 Power Electronics (F)
ECE 482 Digital Signal Processing (F) 

ORF 309* Probability and Stochastic Systems (F & S) is required, plus two or three courses from this list.
Then two or three courses from the list.*ECE 346 Intelligent Robotic Systems (S)
ECE 364*** Machine Learning for Predictive Data Analysis (F)
ECE 368 Intro to Wireless Communication Systems (S)
ECE 381 Networks: Friends, Money and Bytes (S)
ECE 382 Probabilistic Systems and Information Processing (S)
ECE/COS 432 Information Security (F & S)
ECE/COS 434**Machine Learning Theory (F)
ECE 435** Machine Learning and Pattern Recognition (F)
ECE 482 Digital Signal Processing (F)
ECE 486 Transmission and Compression of Information
COS 302/ECE305** Mathematics for Numerical Computing & ML (F)
COS 324*** Introduction to Machine Learning (F)
COS 402** Artificial Intelligence
COS 424** Fundamentals of Machine Learning (S)
COS 429 Computer Vision (F)
ORF 350*** Analysis of Big Data (S)
ORF 363 Computing and Optimization for the Physical and Social Sciences (also COS 323) (F)* ORF 309 can fulfill either the 300level math requirement, or serve as one of the 3 Data and Information courses, but not both.
 If ORF 309 is taken to fulfill the 300level math requirement, take 2 ECE courses from this list, plus any other course on this list. **2 of these 3 courses can be machine learning but not all three.
 If ORF 309 is taken as one of the 3 D&I courses (implying another 300level math course) take any 2 ECE courses from the list.
***Only one of the following machine learning courses may be applied to this concentration since they have overlapping content: ECE364, ORF350 and COS324.


Required:
COS/ECE 375 Computer Architecture and Organization (S)
Two courses from:
ECE 368 Intro to Wireless Communication Systems (S)
ECE 462 Design of VLSI (F)
ECE 464 Embedded Computing (S)
ECE 470 Principles of Blockchains (F)
ECE 472 Architecture for Secure Computers/Smartphones (S)
ECE 475 Computer Architecture (S)
COS 318 Operating Systems (F)
COS 320 Compiling Techniques (S)
COS 461 Computer Networks (S) 

Three courses from:
ECE 345 Intro to Robotics (F)
ECE 346 Intelligent Robotic Systems (S)
ECE 304 Electronic Circuits: Devices to ICs (S)
COS/ECE 375 Computer Architecture and Organization (F)
ECE 364** Machine Learning for Predictive Data Analysis (F)
ECE 435** Machine Learning and Pattern Recognition (F)
ECE 464 Embedded Computing (S)
ECE 481 Power Electronics (F)
COS 324** Introduction to Machine Learning (F)
COS 402** Artificial Intelligence
COS 429 Computer Vision (F)
MAE 433 Automatic Control Systems (F)****Only one Machine Learning course may be used for this concentration.


Required:
ECE 342** Principles of Quantum Engineering (S)
Two courses from:
ECE 396 Introduction to Quantum Computing (F)
ECE 441 SolidState Physics I (F)
ECE 453 Optical & Quantum Electronics (F)
ECE 456 Quantum Optics (S)
ECE 457 Experimental Methods in Quantum Computing (F)
ECE 568 Implementations of Quantum Information (F)**PHY 208 and 305 can be taken in lieu of ECE 342, but are counted as one course for the concentration requirement.


Required:
COS/ECE 432 Information Security (F & S)
Two courses from:
COS/ECE 375 Computer Architecture and Organization (F)
ECE 364** Machine Learning for Predictive Data Analysis (F)
ECE 435** Machine Learning and Pattern Recognition (F)
ECE 464 Embedded Computing (S)
ECE 470 Principles of Blockchains (F)
ECE 472 Architecture for Secure Computers/Smartphones (S)
COS 324** Introduction to Machine Learning (F, S)
COS 402** Artificial Intelligence
COS 424** Fundamentals of Machine Learning
COS 433 Cryptography (S)
COS 461 Computer Networks (S)**Only one Machine Learning course may be applied towards this concentration.


Required:
ECE 445 SolidState Electronic Devices (F) (Prereq is ECE308**)
Two courses from:
ECE 304* Electronic Circuits: Devices to ICs (S)
ECE 308** Electronic and Photonic Devices (F)
ECE 342 Principles of Quantum Engineering (S)
ECE 431 Solar Energy Conversion (F)
ECE 441 SolidState Physics I (F)
ECE 449 MicroNanofabrication and ThinFilm Processing (S)
ECE 481* Power Electronics (F)
ECE 557 Solar Cells (not offered '21'22)
MAE 324 Structure and Properties of Materials (F)
MAE 424 Energy Storage Systems (S)
MSE 301 Materials Science and Engineering (S)
MSE 302 Laboratory Techniques in Materials Science (F)
MSE 505 Characterization of Materials (S)* Only one circuits (304 or 481) course may be applied towards this concentration.
**ECE308 does not count if taken as part of the Foundation Requirement 

Three courses from:
ECE 304 Electronic Circuits: Devices to ICs (S)
ECE 452 Biomedical Imaging (S)
ECE 480 fMRI Decoding: Reading Minds (S '23)
COS 429 Computer Vision (F)
COS 455 Genomics & Computational Molecular Biology (F)
MAE 344 Biomechanics and Biomaterials (S)
NEU 427 Systems Neuroscience (S)
NEU 437 Computational Neuroscience (S) 

Required:
ECE 351 Foundations of Modern Optics (F)
Two courses from:
ECE 342 Principles of Quantum Engineering (S)
ECE 452 Biomedical Imaging (S)
ECE 453 Optical & Quantum Electronics (F)
ECE 455 Optical and Photonic Systems for Environmental Sensing (F)
ECE 458 Photonics and Light Wave Communications (F)
ECE 456 Quantum Optics (S)
MAE 521 Optics and Lasers (F) 

Three courses from:
ECE 445 SolidState Electronic Devices (F)
ECE 431 Solar Energy Conversion (not offered '21'22)
ECE 455 Optical and Photonic Systems for Environmental Sensing (S)
ECE 481 Power Electronics (F)
ECE 557 Solar Cells: Physics, Materials, and Technology (not offered '2122)
MAE 424 Energy Storage Systems
Curriculum Outline
Year  Core Courses  Electives 

1  2 x calculus  3 x general electives 
2 x physics  
chemistry  
computer science  
2  Multivariable Calculus and Linear Algebra  Logic Design and/or Electronic and Photonic Devices 
Electronic Circuits  3 x general electives  
Information and Signals  1 x departmental elective  
3  Building Cyberphysical Systems  4 x general elective 
3 x departmental electives  
1 x math elective  
4  2 x Senior Thesis  5 x general electives 
2 x departmental electives 