## EE 17N: Engineering the Micro and Nano Worlds: From Chips to Genes

Preference to freshmen. The first part is hands-on micro- and nano-fabrication including the Stanford Nanofabrication Facility (SNF) and the Stanford Nanocharacterization Laboratory (SNL) and field trips to local companies and other research centers to illustrate the many applications; these include semiconductor integrated circuits ('chips'), DNA microarrays, microfluidic bio-sensors and microelectromechanical systems (MEMS). The second part is to create, design, propose and execute a project. Most of the grade will be based on the project. By the end of the course you will, of course, be able to read critically a New York Times article on nanotechnology. More importantly you will have experienced the challenge (and fun) of designing, carrying out and presenting your own experimental project. As a result you will be better equipped to choose your major. This course can complement (and differs from) the seminars offered by Profs Philip Wong and Hari Manoharan in that it emphasizes laboratory work and an experimental student-designed project. Prerequisites: high-school physics.

Terms: Spr
| Units: 3
| UG Reqs: GER:DB-EngrAppSci

## EE 21N: What is Nanotechnology?

Nanotechnology is an often used word and it means many things to different people. Scientists and Engineers have some notion of what nanotechnology is, societal perception may be entirely different. In this course, we start with the classic paper by Richard Feynman ("There's Plenty of Room at the Bottom"), which laid down the challenge to the nanotechnologists. Then we discuss two classic books that offer a glimpse of what nanotechnology is: Engines of Creation: The Coming Era of Nanotechnology by Eric Drexler, and Prey by Michael Crichton. Drexler's thesis sparked the imagination of what nano machinery might do, whereas Crichton's popular novel channeled the public's attention to this subject by portraying a disastrous scenario of a technology gone astray. We will use the scientific knowledge to analyze the assumptions and predictions of these classic works. We will draw upon the latest research advances to illustrate the possibilities and impossibilities of nanotechnology.

Terms: Spr
| Units: 3
| UG Reqs: GER:DB-EngrAppSci, WAY-SMA

## EE 64SI: Mechanical Prototyping for Electrical Engineers

This course will give non-mechanical engineers experience designing mechanical assemblies specifically for manufacture by readily accessible tools, such as 3-D printers and laser cutters. It will also teach students to debug their own mechanical designs, and interface them with other components (such as store-bought parts). By the end of the quarter students will feel comfortable independently designing and manufacturing simple assemblies to serve useful functions in their lives.

Terms: Spr
| Units: 2

## EE 65: Modern Physics for Engineers

This course introduces the core ideas of modern physics that enable applications ranging from solar energy and efficient lighting to the modern electronic and optical devices and nanotechnologies that sense, process, store, communicate and display all our information. Though the ideas have broad impact, the course is widely accessible to engineering and science students with only basic linear algebra and calculus through simple ordinary differential equations as mathematics background. Topics include the quantum mechanics of electrons and photons (Schrödinger's equation, atoms, electrons, energy levels and energy bands; absorption and emission of photons; quantum confinement in nanostructures), the statistical mechanics of particles (entropy, the Boltzmann factor, thermal distributions), the thermodynamics of light (thermal radiation, limits to light concentration, spontaneous and stimulated emission), and the physics of information (Maxwell¿s demon, reversibility, entropy and noise in physics and information theory). Pre-requisite:
Physics 41. Pre- or co-requisite:
Math 53 or
CME 102.

Terms: Spr
| Units: 4
| UG Reqs: GER: DB-NatSci, GER:DB-EngrAppSci, WAY-SMA

## EE 76: Information Science and Engineering

What is information? How can we measure and efficiently represent it? How can we reliably communicate and store it over media prone to noise and errors? How can we make sound decisions based on partial and noisy information? This course introduces the basic mathematics required to formulate and answer these questions, as well as some of the principles and techniques in the design of modern information, communication, and decision-making systems. Students will also get a glimpse of ways in which these principles manifest in domains ranging from the neural codes of the brain, through the genetic code, to the structure of human language.

Terms: Spr
| Units: 4

## EE 101B: Circuits II

Continuation of
EE101A. Introduction to circuit design for modern electronic systems. Modeling and analysis of analog gain stages, frequency response, feedback. Filtering and analog¿to¿digital conversion. Fundamentals of circuit simulation. Prerequisites:
EE101A,
EE102A. Recommended:
CME102.

Terms: Spr
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-SMA

Instructors:
Murmann, B. (PI)
;
Wong, S. (PI)

## EE 102B: Signal Processing and Linear Systems II

Continuation of
EE 102A. Concepts and tools for continuous- and discrete-time signal and system analysis with applications in communications, signal processing and control. Analog and digital modulation and demodulation. Sampling, reconstruction, decimation and interpolation. Finite impulse response filter design. Discrete Fourier transforms, applications in convolution and spectral analysis. Laplace transforms, applications in circuits and feedback control. Z transforms, applications in infinite impulse response filter design. Prerequisite:
EE 102A.

Terms: Spr
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

Instructors:
Goldsmith, A. (PI)
;
Strong, C. (TA)

## EE 104: Introduction to Machine Learning (CME 107)

Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites:
EE 103;
EE 178 or
CS 109; CS106A or equivalent.

Terms: Spr
| Units: 3-5

Instructors:
Lall, S. (PI)
;
Tuck, J. (TA)

## EE 109: Digital Systems Design Lab

The design of integrated digital systems encompassing both customized software and hardware. Software/hardware design tradeoffs. Algorithm design for pipelining and parallelism. System latency and throughput tradeoffs. FPGA optimization techniques. Integration with external systems and smart devices. Firmware configuration and embedded system considerations. Enrollment limited to 25; preference to graduating seniors. Prerequisites: 108B, and
CS 106B or X.

Terms: Spr
| Units: 4

Instructors:
Olukotun, O. (PI)

## EE 142: Engineering Electromagnetics

Introduction to electromagnetism and Maxwell's equations in static and dynamic regimes. Electrostatics and magnetostatics: Gauss's, Coulomb's, Faraday's, Ampere's, Biot-Savart's laws. Electric and magnetic potentials. Boundary conditions. Electric and magnetic field energy. Electrodynamics: Wave equation; Electromagnetic waves; Phasor form of Maxwell's equations.nSolution of the wave equation in 1D free space: Wavelength, wave-vector, forward and backward propagating plane waves.Poynting's theorem. Propagation in lossy media, skin depth. Reflection and refraction at planar boundaries, total internal reflection. Solutions of wave equation for various 1D-3D problems: Electromagnetic resonators, waveguides periodic media, transmission lines. Formerly
EE 141. Pre-requisites: Phys 43 or
EE 42,
CME 100,
CME 102

Terms: Spr
| Units: 3
| UG Reqs: GER:DB-EngrAppSci, WAY-FR, WAY-SMA

Instructors:
Yuen, M. (PI)
;
Neustock, L. (TA)

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