121 - 130 of 203 results for: EE

The class teaches cutting-edge optimization and analysis algorithms for the design of complex digital integrated circuits and their use in designing machine learning hardware. It provides working knowledge of the key technologies in Electronic Design Automation (EDA), focusing on synthesis, placement and routing algorithms that perform the major transformations between levels of abstraction and get a design ready to be fabricated. As an example, the design of a convolutional neural network (CNN) for basic image recognition illustrates the interaction between hardware and software for machine learning. It will be implemented on a state-of-the-art FPGA board.

As digital systems grow increasingly complex, traditional design methods struggle to keep up. This research seminar delves into the transformative impact of AI, with an emphasis on generative AI, in advancing productivity and efficiency in digital system design. Students will gain insights into AI-boosted hardware design enabled by recent advancements in hardware verification, alongside related topics such as AI-enhanced architectures for processors and accelerators, machine learning applications in system design, and AI-based innovations for improving manufacturing quality. Exciting projects on AI-boosted digital system AI-boosted Digital System design will be discussed.

Fundamental aspects of CVD are initially considered, first focusing on processes occurring in the gas phase and then on those occurring on the surface. Qualitative understanding is emphasized, with minimal use of equations. Adding energy both thermally and by using a plasma is discussed; atomic-layer deposition is briefly considered. Examples of CVD equipment are examined. The second portion of the tutorial examines layers deposited by CVD. The focus is on group IV semiconductors especially epitaxial and heteroepitaxial deposition, in which the crystal structure of the depositing layer is related to that of the substrate. Polycrystalline silicon and the IC interconnect system are then discussed. Finally, the use of high-density plasmas for rapid gap filling is contrasted with alternative CVD dielectric deposition processes.

This is a project-based class where students will learn how to develop machine learning models for execution in resource constrained environments such as embedded systems. In this class students will learn about techniques to optimize machine learning models and deploy them on a device such as a Arduino, Raspberry PI, Jetson, or Edge TPUs. The class has a significant project component. Prerequisites: CS 107(required), CS 229 (recommended), CS 230 (recommended).

Seminar. For engineering students interested in camera and display engineering, computer vision, and computational imaging. Speakers include Stanford faculty and research scientists as well as industry professionals, mostly from consumer electronics companies.

This course presents the application of rigorous digital image processing to problems in visualization and understanding of fine paintings, drawings, and other two-dimensional artworks. It builds upon a wealth of techniques but modifies and applies them to cases of interest to the technical art community. Such techniques include transforms such as DCT and wavelets, color quantization, blind source (image) separation, edge detection, super-resolution, visual style learning and transfer, digital in-painting, color transforms, level-set analysis, estimation of region statistics, Affine image transforms, and many others. Students will perform several projects which will involve coding, mathematical/statistical analysis, and explaining the relevance of the work to art scholarship.

Historically, piezoelectric materials (e.g., PZT and Quartz) have been the backbone of many electroacoustic technologies, spanning from sonar and phonographs to signal filters for TV/radio and gas igniters. Some of these technologies have been subjected to constant improvements and miniaturization throughout the decades, but their dependency of bulk piezoelectric materials to operate has impaired their integration with electronics. The adoption of microelectromechanical systems (MEMS) manufacturing methods together with the advent of sputtering-based piezoelectric materials (e.g., AlN) has transformed the field of piezoelectric sensors and resonators to the point that now all these technologies can be mass manufactured and packaged in small form-factor chips. In this course, you will learn the basics behind the design, fabrication, characterization, and packaging of piezoelectric MEMS, as well as their most successful commercial applications in the areas of sensing, RF, and power management. We will review the fundamentals of elasticity that will be recurrently used throughout the course. To determine the sensitivity of piezoelectric transducers, we will derive the Euler-Bernoulli and Sophie-German equations that respectively determine elasticity in flexural beams and plates, including the effect of residual film stress. Regarding dynamic analysis, we will dive into the mathematical tools needed to compute the effective mass and natural resonance frequency of vibrating rectangular slabs (e.g., Rayleigh's energy method) and uncover the main damping mechanisms that limit their Q factor. The piezoelectric effect will be the transduction mechanism of focus, so you will learn relevant material science concepts associated with it, such as piezoelectric stiffening and electromechanical coupling. As examples of piezoelectric MEMS sensors, this course will cover piezoelectric MEMS microphones, Quartz crystal microbalances (QCMs) and piezoelectric micromachined ultrasound transducers (PMUTs). In RF, the holy grail of piezoelectric MEMS is bulk acoustic wave (BAW) resonators with its different flavors: film bulk acoustic resonators (FBAR) and solidly mounted resonators (SMR). We will learn their linear and nonlinear modeling circuit techniques and how they can be arranged with electronics to form filters and oscillators. Finally, we will explore two emerging areas of development in power management using piezoelectric MEMS: piezoelectric energy harvesters and piezoelectric DC-DC converters, which are key to supply energy to low and high power systems, respectively. All this knowledge will be complemented with Finite Element Modeling (FEM) using COMSOL Multiphysics and put in practice in real MEMS technology applications

The purpose of this seminar series course is to help students and professionals develop the tools to apply the engineering and entrepreneurial mindset to problems that stem from climate change, in order to consider and evaluate possible stabilizing, remedial and adaptive approaches. This course is not a crash course on climate change or policy. Instead we will focus on learning about and discussing the climate problems that seem most tractable to these approaches. Each week Dr. Field and/or a guest speaker will lead a short warm-up discussion/activity and then deliver a talk in his/her area of expertise. We will wrap up with small-group and full-class discussions of related challenges/opportunities and possible engineering-oriented solutions. Class members are asked to do background reading before each class, to submit a question before each lecture, and to do in-class brainstorming. May be repeated for credit.

Great products are crafted by product teams, typically comprising engineering, product management, and customer support. We start by identifying unmet market needs and then satisfying them through an iterative process that builds from functional infancy to market leadership. In this class, we aim to demystify the process by directly speaking with guests who've delivered highly successful products. We aim to introduce how great hardware and software products are crafted, both in startups and in larger companies. Students will learn why pursuing areas of interest and curiosity is critical to building world-class solutions to problems. Previous companies profiled: Intel, Apple, HP, Microsoft, VMWare, Genentech, Blue Bottle Coffee, Pixar, and Pivotal Labs -- to name a few. Previous guests include Ted Hoff (Inventor of the microprocessor and employee #12 at Intel), Diane Greene (Co-founder and CEO of VMware, former President of Google Cloud, and former Chair of The MIT Corporation), Rob Mee (Co-Founder of Pivotal Labs and Founder of Mechanical Orchard), Evans Hankey (former VP of Design at Apple, Co-Founder Io - acquired by OpenAI), Matt Kraning (EE292i Alumnus, Co-Founder Expanse - acquired by Palo Alto Networks - now General Partner, Menlo Ventures. Pre-requisites: None.