EEE 4351C Semiconductor Devices is carried at the University of South Florida at 3 credits. It is the course in which the electrical properties of solids are developed from physics and then used to explain, quantitatively, why a diode rectifies and a transistor amplifies.
The Statewide Course Numbering System titles the number Solid-State Electronic Devices and describes it as covering “solid-state physics as applied to electronic devices. Semiconductor materials, conduction processes in solids, device fabrication, diffusion processes, and negative conduction devices.” The FAMU–FSU College of Engineering, which carries the unsuffixed EEE 4351, gives the same description and sets prerequisites of EEE 3300 and EEE 3300L.
⚠⚠ Two warnings about this number before anything else.
First, the suffix. Only USF carries EEE 4351C. FAMU and FSU carry the unsuffixed EEE 4351 under the title Solid-State Electronic Devices, and Florida Polytechnic carries EEE 3351 Electronic Devices at a lower level. These are separate SCNS records, and equivalency does not cross the suffix.
Second, and more confusing: the statewide title Solid-State Electronic Devices sits on two different course numbers. It is the statewide title for EEE 4351 and also for EEE 3396. UF and UWF both teach EEE 3396 under that exact title. So a student searching Florida catalogs for “Solid-State Electronic Devices” will find it at two levels under two numbers — and a transfer evaluator matching on title alone will conflate them. Read the level digit and the description, not the title.
The C suffix on USF’s version indicates an integrated lecture-and-laboratory course, so expect device characterisation alongside the theory.
This number sits in the most confusing corner of the EEE prefix. Three distinct problems overlap:
| Number | Institution | Institutional title | Statewide title |
|---|---|---|---|
| EEE 4351C | USF | Semiconductor Devices | Solid-State Electronic Devices |
| EEE 4351 | FAMU, FSU | Solid-State Electronic Devices | Solid-State Electronic Devices |
| EEE 3396 | UF, UWF | Solid-State Electronic Devices | Solid-State Electronic Devices |
| EEE 3351 | Florida Poly | Electronic Devices | — |
What to do: carry the syllabus, and when requesting a substitution, present the topic list rather than the number or the title. Departments approve these routinely on content; the obstacle is always evidence, not merit.
At USF, EEE 4351C Semiconductor Devices and EEE 4314C Integrated Circuit Technology both exist, and their content overlaps substantially — both cover p-n junctions, bipolar transistors, contacts and MOS systems. They are nonetheless distinct requirements. Check your degree audit to see which your programme requires, and do not assume one covers the other.
The statewide description names negative conduction devices explicitly, which is uncommon in a modern undergraduate devices course. This refers to devices exhibiting negative differential resistance — the tunnel (Esaki) diode, the Gunn diode and IMPATT devices — where current decreases as voltage increases over part of the characteristic, which allows a two-terminal device to sustain oscillation.
Two practical notes. These devices are less prominent in current practice than they were when the statewide description was written, so instructors vary in how much time they give them — some cover them substantially, others in a single lecture. And they are thinly covered in the standard undergraduate textbooks; Sze and Ng is the usual reference if you need more than the lecture provides. If you are interested in RF and microwave sources, this is the part of the course to pay attention to.
At FAMU and FSU the prerequisites for the unsuffixed number are EEE 3300 and EEE 3300L — Electronics I and its laboratory. USF’s requirements differ; check your own catalog.
What the prerequisite stands for is knowing what devices do before studying why. ⚠ The unnamed requirement is mathematical maturity of a specific kind. This course integrates across a depletion region, solves the continuity equation with boundary conditions, and works constantly with exponential carrier distributions. It is calculus applied to physical distributions rather than algebra applied to circuits — a genuine step change for students who found circuits comfortable because the mathematics was procedural. Comfort with exponentials, error functions and boundary-value problems is the practical requirement, and reviewing them beforehand pays.
EEE 4351C is a senior-level course in the microelectronics track. It follows a first electronics course and precedes integrated-circuit design, fabrication and nanotechnology electives, and it is the standard preparation for graduate study in devices. At FAMU and FSU, the unsuffixed EEE 4351 sits alongside EEE 4330 Microelectronics Engineering (the fabrication counterpart) and EEE 4450 (device modelling and simulation); the three together make a coherent devices concentration.
USF carries this as an integrated lecture-and-laboratory course at 3 credits, so expect roughly five contact hours a week rather than three — the usual trap with C courses, where students budget by credit value and are caught by scheduled hours. The laboratory component in a devices course typically involves I–V and C–V characterisation and parameter extraction on a parameter analyser, which is directly relevant experience for device and process roles.
This is a demanding course, and its characteristic difficulty is the length of its derivations. The ideal diode equation, the bipolar current gain and the MOSFET threshold voltage each take a page or more, and a student who memorises endpoints without following the argument cannot adapt them — which is exactly what examinations require. Plan on nine to eleven hours a week and work each major derivation by hand at least twice.
The single highest-value skill is drawing energy band diagrams under bias. Most questions in this subject can be answered from a correct band diagram, and most wrong answers begin with an incorrect one. Practise them until they are automatic; it is the best return on effort the course offers.
SCNS records the course as guaranteed to transfer to an institution offering the same course; only USF offers this exact suffixed number, so read that narrowly. The course is upper-division, carries 3 credits, and has no general-education or Gordon Rule designation.
The NCEES Fundamentals of Engineering (Electrical and Computer) exam covers semiconductor materials and devices within its Electronics topic area: band gaps, doping, carrier transport, junction behaviour and transistor operation. This course covers that material considerably more deeply than the exam requires. The FE tests application rather than derivation, so a targeted review of the summary relationships remains worthwhile.
Device physics is an area where AI assistance is useful for explanation and consistently unreliable for values, and the boundary is unusually clean.
Where AI is used in the discipline. Machine learning is in production use in semiconductor manufacturing for yield prediction, defect classification and virtual metrology, and learned optimisation is increasingly used in compact model parameter extraction — historically a laborious curve-fitting exercise. Materials and device screening by machine learning is an active research method. A graduate entering device or process engineering will encounter these systems, and being able to interrogate what they fit is part of the qualification.
Where a general-purpose assistant helps in coursework. Explaining why the depletion region widens under reverse bias, or what inversion physically means, in different words from the textbook; walking through the algebra of a long derivation; generating plotting scripts for band diagrams, carrier profiles and C–V curves; and explaining an unfamiliar SPICE model parameter or an obscure device acronym.
⚠ Where it fails, and why the failure is precisely this course’s subject. The characteristic error is that a model supplies a device parameter without the conditions that define it — a mobility, a threshold voltage, a saturation current, a breakdown voltage — when the entire content of this course is that these are functions, not constants. Mobility depends on doping, temperature and field. Threshold voltage depends on oxide thickness, substrate doping and body bias. Saturation current depends exponentially on temperature. A confident number with no conditions attached is the exact misconception the course exists to dismantle, and it is what a fluent generated answer supplies.
Two further failures recur. Models reliably apply the long-channel square-law MOSFET model to short-channel devices, producing predictions wrong by large factors — and short-channel behaviour is precisely the modern content of the course. And they conflate the Fermi level with the highest occupied state, which is true only at absolute zero, then reason from that error to wrong conclusions about doped material at room temperature. Because this is also a common student misconception, a generated answer repeating it is unusually persuasive and unusually damaging.
Models also produce qualitatively wrong band diagrams — bending the wrong way under bias, or misplacing the Fermi level in a biased region. Since the band diagram is the reasoning tool of the entire subject, an incorrect one corrupts every conclusion drawn from it.
The engineer’s responsibility. A device parameter used in a design is a claim about behaviour under stated conditions of temperature, bias and process, and the engineer signs for the conditions as much as for the number. The habit to form here is to ask of every value: at what temperature, at what doping, at what bias, and measured how? A parameter database or a foundry model card answers those questions; a generated number generally does not. And draw the band diagram yourself — it is the cheapest and most reliable check this subject offers.
Academic integrity. USF, FAMU and FSU each maintain academic integrity policies covering AI-generated work, and practice varies by instructor. Derivations are normally expected to be your own even where computational assistance is permitted. In the integrated form, generated laboratory or characterisation data is data fabrication — treated more seriously than plagiarism. Ask before you rely on a tool, and disclose its use where the syllabus requires it.
Generated September 9, 2026 · Updated September 9, 2026