## EE 195: Electrical Engineering Instruction

Students receive training from faculty or graduate student mentors to prepare them to assist in instruction of Electrical Engineering courses. The specific training and units of credit received are to be defined in consultation with one of the official instructors of
EE 195. Note that University regulations prohibit students from being paid for the training while receiving academic credit for it. Enrollment limited.

Terms: Aut, Win, Spr
| Units: 1-3

Instructors:
Dutton, R. (PI)
;
Tobagi, F. (PI)

## EE 207: Neuromorphics: Brains in Silicon (BIOE 313)

(Formerly
EE 304) Neuromorphic systems run perceptual, cognitive and motor tasks in real-time on a network of highly interconnected nonlinear units. To maximize density and minimize energy, these units--like the brain's neurons--are heterogeneous and stochastic. The first half of the course covers learning algorithms that automatically synthesize network configurations to perform a desired computation on a given heterogeneous neural substrate. The second half of the course surveys system-on-a-chip architectures that efficiently realize highly interconnected networks and mixed analog-digital circuit designs that implement area and energy-efficient nonlinear units. Prerequisites: EE102A is required.

Terms: Spr
| Units: 3

Instructors:
Boahen, K. (PI)

## EE 216: Principles and Models of Semiconductor Devices

Carrier generation, transport, recombination, and storage in semiconductors. Physical principles of operation of the p-n junction, heterojunction, metal semiconductor contact, bipolar junction transistor, MOS capacitor, MOS and junction field-effect transistors, and related optoelectronic devices such as CCDs, solar cells, LEDs, and detectors. First-order device models that reflect physical principles and are useful for integrated-circuit analysis and design. Prerequisite: 116 or equivalent.

Terms: Aut, Sum
| Units: 3

## EE 236C: Lasers

Atomic systems, spontaneous emission, stimulated emission, amplification. Three- and four-level systems, rate equations, pumping schemes. Laser principles, conditions for steady-state oscillation. Transverse and longitudinal mode control and tuning. Exemplary laser systems: gas (HeNe), solid state (Nd:YAG, Ti:sapphire) and semiconductors. Elements of laser dynamics and noise. Formerly
EE231. Prerequisites:
EE 236B and familiarity with modern physics and semiconductor physics. Recommended:
EE 216 and
EE 223 (either may be taken concurrently).

Terms: Spr
| Units: 3

Instructors:
Heinz, T. (PI)

## EE 237: Solar Energy Conversion

This course will be an introduction to solar photovoltaics. Basics of solar energy conversion in photovoltaic devices. Economics of solar energy. Solar cell device physics: electrical and optical. Different generations of photovoltaic technology: crystalline silicon, thin film, multi-junction solar cells. Perovskite and silicon tandem cells. Advanced energy conversion concepts like photon up-conversion, quantum dot solar cells. Solar system issues including module assembly, inverters, and micro-inverters. Guest speakers include distinguished engineers, entrepreneurs and venture capitalists actively engaged in solar industry. No prior photovoltaics knowledge is required. Recommended:
EE116, EE216 or equivalent.

Terms: Spr
| Units: 3

Instructors:
Saraswat, K. (PI)

## EE 238: Introduction to Fourier Optics

Fourier analysis applied to optical imaging. Theoretical topics include Fourier transform and angular spectrum to describe diffraction, Fourier transforming properties of lenses, image formation with coherent and incoherent light and aberrations. Application topics will cover image deconvolution/reconstruction, amplitude and phase pupil engineering, computational adaptive optics, and others motivated by student interest. Prerequisites: familiarity with Fourier transform and analysis,
EE 102 and
EE 142 or equivalent.

Terms: Spr
| Units: 3

Instructors:
Dubra, A. (PI)

## EE 254: Advanced Topics in Power Electronics

In this course, we will study the practical issues related to the practical design of power electronic converters. We will also explore the trade-offs involved in selecting among the different circuits used to convert ac to dc, dc to ac and back to dc over a wide range of power levels suitable for different applications. In Advanced Topics in Power Electronic, as a multidisciplinary field, we will discuss power electronics circuits, extraction of transfer functions in Continuous and discontinuous conduction mode, voltage and current control of power converters, design of input/output filters to meet Electro Magnetic Interference specifications, layout of power electronics circuits and put this knowledge in a very practical context. Prerequisites:
EE 153/253.

Terms: Spr
| Units: 3

Instructors:
Rivas-Davila, J. (PI)

## EE 261: The Fourier Transform and Its Applications

The Fourier transform as a tool for solving physical problems. Fourier series, the Fourier transform of continuous and discrete signals and its properties. The Dirac delta, distributions, and generalized transforms. Convolutions and correlations and applications; probability distributions, sampling theory, filters, and analysis of linear systems. The discrete Fourier transform and the FFT algorithm. Multidimensional Fourier transform and use in imaging. Further applications to optics, crystallography. Emphasis is on relating the theoretical principles to solving practical engineering and science problems. Prerequisites: Math through ODEs, basic linear algebra, Comfort with sums and discrete signals, Fourier series at the level of 102A

Terms: Aut, Sum
| Units: 3

Instructors:
Osgood, B. (PI)
;
Zhou, Z. (PI)

## EE 263: Introduction to Linear Dynamical Systems (CME 263)

Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: least-squares approximations of over-determined equations, and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singular-value decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices; convolution and transfer-matrix descriptions. Control, reachability, and state transfer; observability and least-squares state estimation. Prerequisites: Linear algebra and matrices as in
EE 103 or
MATH 104; ordinary differential equations and Laplace transforms as in
EE 102B or
CME 102.

Terms: Aut, Sum
| Units: 3

Instructors:
Aggarwal, G. (PI)
;
Lall, S. (PI)

## EE 264: Digital Signal Processing

Digital signal processing (DSP) techniques and design of DSP applications. Topics include: discrete-time random signals; sampling and multi-rate systems; oversampling and quantization in A-to-D conversion; properties of LTI systems; quantization in fixed-point implementations of filters; digital filter design; discrete Fourier Transform and FFT; spectrum analysis using the DFT; parametric signal modeling and adaptive filtering. The course also covers applications of DSP in areas such as speech, audio and communication systems. The optional lab section (Section 02) provides a hands-on opportunity to explore the application of DSP theory to practical real-time applications in an embedded processing platform. See
ee264.stanford.edu for more information. Register in Section 02 to take the lab. Undergraduate students taking the lab should register for 4 units to meet the EE design requirement. The optional lab section is not available to remote SCPD students. Prerequisites:
EE 102A and
EE 102B or equivalent, basic programming skills (Matlab and C++)

Terms: Win, Sum
| Units: 3-4

Instructors:
Mujica, F. (PI)
;
Schafer, R. (PI)

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