51 - 60 of 203 results for: EE

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.

The course introduces students to the fundamentals of product management. Topics start with understanding customers and identifying the jobs that they need to get done. Next, possible solutions for these jobs are identified and tested. Finally, with real need and solutions verified, we assess technical feasibility, quantify the value to the customer, and market size. After presenting our new product recommendations to the corporate partner. And with their approval, we move forward and develop an MVP, business model, and what's next plan. We are one of the highest-rated classes on Carta across Stanford and only offered once a year. The class experience consists of two components: A hands-on product management project with a corporate partner where teamwork will challenge and reward your efforts: https://docs.google.com/document/d/1ObGj9mZb5-eW3WBQub7YqqeD0OvpbIJKyKu7KkMPnjg/edit?tab=t.0 Case-based classroom discussions of product management concepts and readings from industry thought leaders: https://canvas-gateway.stanford.edu/goCanvas.html You should take this class if you want: Real, hands-on experience in new product discovery and development; Applying advanced engineering and research to real-world product opportunities; Learn to take a product idea from initial hypothesis through validation, prototyping, and business modeling; Explore career paths that lead to product management roles; Work with real world companies on exciting projects and see them get deployed in the market Corporate Partners for Winter 2026: FANUC (one of worlds largest robotic company), Gruve AI (leading AI software company), Ninja AI (best in class AI agent operator layer), Verra Mobility (publically traded fintech company in the mobility space) and Doximity (Linkedin for Doctors). Application required - Google link (takes 5 minutes): https://docs.google.com/forms/d/e/1FAIpQLSeVs9XbKP-RAUvjwA2K31KVl639I6yWF9mI2vi--aJ8kVFFWg/viewform?usp=send_form Deadline for application: Nov 21st

While traversing through the natural world, you effortlessly perceive and react to a rich stream of stimuli. This constantly changing stream evokes spatiotemporal patterns of spikes that propagate through your brain from one ensemble of neurons to another. An ensemble may memorize a spatiotemporal pattern at the speed of life and recall it at the speed of thought. In the first half of this course, we will discuss and model how a neural ensemble memorizes and recalls such a spatiotemporal pattern. In the second half, we will explore how neuromorphic hardware could exploit these neurobiological mechanisms to run AI not with megawatts in the cloud but rather with watts on a smartphone. Prerequisites: Either computational modeling ( BIOE 101, BIOE 300B) or circuit analysis ( EE 101A).

For students interested in the physical bases and practical methods of silicon VLSI chip fabrication, or the impact of technology on device and circuit design, or intending to pursue doctoral research involving the use of Stanford's Nanofabrication laboratory. Process simulators illustrate concepts. Topics: principles of integrated circuit fabrication processes, physical and chemical models for crystal growth, oxidation, ion implantation, etching, deposition, lithography, and back-end processing. Required for 410.

Analysis and simulation of elementary transistor stages, current mirrors, supply- and temperature-independent bias, and reference circuits. Overview of integrated circuit technologies, circuit components, component variations and practical design paradigms. Differential circuits, frequency response, and feedback will also be covered. Performance evaluation using computer-aided design tools. Undergraduates must take EE 114 for 4 units. GER:DB-EngrAppSci

Analysis and design of analog and digital integrated circuits in advanced CMOS technology. Emphasis on compact modeling of performance limiting aspects and intuitive approaches to design. Analytical treatment of noise; analog circuit sizing using the transconductance to current ratio; analysis and design of feedback circuits. Delay analysis of digital logic gates; decoder design using logical effort. CMOS image sensors are used as a motivating application example.

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.

This course provides an introduction to the sensor systems found in modern-day smartphones, wearables, and hearable devices. As much as we take their functionality for granted, there is a tremendous amount of engineering needed to sense "real world" signals such as acceleration, touch, or altitude. There will be an overview on the actual circuitry and hardware used in sensor implementations, with a focus on MEMS devices (eg, accelerometer/gyro), going up through the algorithms commonly seen in sensors processing, and finally fusion of data from multiple sensors to yield final data presented to a user. The four broad areas that will be covered are: Inertial sensing/movement; Touch sensing/authentication; Health sensing (PPG, ECG, SpO2); Next-generation (force, radar/ranging, ultrasonics, and more). There is a lab/project associated with each of these areas, each project spanning roughly two weeks. The projects are designed to be more at a system level; the student will be required to explore the performance and limitations of sensing hardware, and then take that understanding to solve real-world sensor problems. All projects will be built on a Raspberry Pi with various sensor boards; students should be comfortable with wiring up a small breadboard, and coding on an RPi a high-level language such as Python or Java.

This course starts by covering the device physics and technology of current silicon power semiconductor devices including power MOSFETs, IGBTs, and Thyristors. Wide bandgap materials, especially GaN and SiC are potential replacements for Si power devices because of their fundamentally better properties. This course explores what is possible in these new materials, and what the remaining challenges are for wide bandgap materials to find widespread market acceptance in power applications. Future clean, renewable energy systems and high efficiency power control systems will critically depend on the higher performance devices possible in these new materials.