REU 2015 Project Descriptions_Final

REU 2015 Project Proposals *You may select up to 8 projects. You also have the option to specify a first choice project.

Project #: 1

Project Title: Soft Electronic Interfaces with Surgical Robots

Faculty Advisor: Jonathan Fan Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Sage Doshay Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: [email protected] Project Description:

Surgical robots are FDA-approved tools used in minimally invasive surgical procedures, which offer shorter recovery times, less blood loss, and less risk of infection compared to conventional surgery options. One such robot is the Da Vinci surgical system, which takes part in over 200,000 procedures a year. There is tremendous interest in the medical community to use these tools in delicate procedures such as heart surgery, which is among the most common surgical procedures performed today. However, these robots lack haptic response and cannot feel their surrounding environment, preventing their use in delicate procedures.

This project will focus on simulating, fabricating, and testing mechanically soft electronic force sensors that can directly interface with a surgical robotic arm. These sensors will be designed to measure both normal and shear forces, and they will be used to help the robotic arm sense surrounding tissue and enhance gripping control. The sensors will be fabricated by using lithographic processes in the cleanroom, followed by transfer printing onto a rubber-like substrate. This project will be done in collaboration with Allison Okamura, a professor in mechanical engineering, who is a world expert in surgical robotics.

Recommended Courses/Readings: Some coursework and familiarity with devices and circuits is preferred

Desired Qualifications of REU Intern: A willingness to sign up for a research course with Prof. Fan prior to the summer to prepare for the project, read background material, and start preliminary experimental work

Maximum number of REU intern positions available: 3 Attachments:

Additional Comments:


Project #: 2

Project Title: Bluetooth Implementation for Communication Between Portable Molecular Diagnostics Device and Smartphone

Faculty Advisor: Shan Wang Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Joohong Choi Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: [email protected] Project Description:

Implement Bluetooth interface for a portable diagnostic device to control the biosensors and to transfer acquired raw biological data from the device to a smartphone.

Recommended Courses/Readings: CS 193A: Android Programming(or Android app programming experience), ME 218C: Smart Product Design Practice(or Microcontroller embedded system experience) + Bluetooth implementation experience is plus

Desired Qualifications of REU Intern: Proficiency in C and Java computer language, Microcontroller embedded system experience, Android app programming experience. Bluetooth implementation experience is plus


Additional Comments: The portable diagnostic device to be implemented with Bluetooth interface was awarded a Distinguished Award in Nokia Sensing XChallenge organized by XPRIZE Foundation.


Project #: 3

Project Title: Innovative Musical Systems

Faculty Advisor: Robert Dutton Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: darrell ford Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Radio systems are everywhere. Your phone and many other portable appliances you use daily have radio sub- systems in them. This project will give you a quick introduction to radios and then have you go into the lab and build several simple circuits that are key parts of radio systems. The research goal will be to have you develop both intuition and hands-on experience with radio-frequency (RF) circuits and techniques (i.e. you really need to have some experience from labeither Engr 40 or EE 101A-B preferred but negotiable). The longer-term goals are targeted at novel applications where minimal radio systems can be developed and deployed for things like sensor-based systemsthere are many venues for their application (i.e. sensors in buildings for safety; healthcare monitoring systems; traffic control systems...and many more)

Recommended Courses/Readings: E40M (or EE 122A)

Desired Qualifications of REU Intern: practical experience with real musical instrument(s)

Maximum number of REU intern positions available: 2 Attachments: Innovative.Music.Systems.2015.pdf (attachment at the end of the document pg. 47)

Additional Comments: N/A


Project #: 4

Project Title: Minimal Radio Systems

Faculty Advisor: Robert Dutton Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: antonio abundes Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Radio systems are everywhere. Your phone and many other portable appliances you use daily have radio sub- systems in them. This project will give you a quick introduction to radios and then have you go into the lab and build several simple circuits that are key parts of radio systems. The research goal will be to have you develop both intuition and hands-on experience with radio-frequency (RF) circuits and techniques (i.e. you really need to have some experience from labeither Engr 40 or EE 101A-B preferred but negotiable). The longer-term goals are targeted at novel applications where minimal radio systems can be developed and deployed for things like sensor-based systemsthere are many venues for their application (i.e. sensors in buildings for safety; healthcare monitoring systems; traffic control systems...and many more)

Recommended Courses/Readings: EE 101A-B (possibly Engr 40)

Desired Qualifications of REU Intern:

Maximum number of REU intern positions available: 2 Attachments: Minimal.Radio.Systems.2015.pdf

Additional Comments: (attachment at the end of the document pg. 48)


Project #: 5

Project Title: Filter PCB Structures for High Frequency Power ConvertersFilter

Faculty Advisor: Juan Rivas-Davila Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Lei Gu Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Wei Wei Graduate Student Mentor Email: [email protected] Project Description:

A significant part of the cost and volume of modern electronic equipment is due to the energy conversion and energy storage systems that they require. A challenge of particular importance, and the subject of this project, is the miniaturization of power electronic circuits. Miniaturization of these systems is difficult because the power conversion process requires passive elements with significant energy storage. Thus, design and manufacturing methods that reduce energy storage requirements are very valuable in reducing the size of power converters. Power inductors and transformers, in particular, are challenging to miniaturize because of their poor performance when scaled down in size, and the difficulty of fabricating them with available planar processes.

A family of approximating networks for transmission lines, the focus of this project, enables miniaturization by internally circulating energy and exchanging delay fidelity for bulk energy storage. These multi-resonant components are substantially smaller than their lumped counterparts, in particular requiring less inductance, and enforce useful waveform symmetries that can be traded for higher power or higher efficiency. Lumped analogs of transmission lines, and delay-based means of processing energy in general, exploit rather than fight the parasitics which can restrict conventional designs to lower switching frequencies, and are compatible with RF power-conversion techniques.

The student will model, simulate and fabricate an input/output filter using Printed Circuit Board components which can emulate the impedance characteristics of a shorted quarter wavelength transmission line for a 10s of MHz resonant converter. The resonant frequencies would be aligned with switching frequency of the converter. If the fundamental switching frequency component and odd harmonic components can be aligned with poles of the input impedance of this filter and the even harmonic components can be aligned with zeros of the input filter, the current and voltage waveforms in the converter will be symmetrical. Furthermore, if we could design the input impedances at different harmonic components in a way that we want, in this case the voltage and current waveforms could also be formed by the input filter before it reaches the core converter.

Recommended Courses/Readings: EE101-A, EE101-B

Desired Qualifications of REU Intern: Basic circuit analysis, use of PCB layout software, basic circuit simulation skills

Maximum number of REU intern positions available: 1


Attachments: 2015REU_filters.pdf (attachment at the end of the document pg. 49-50)



Project #: 6

Project Title: 3D Modeling and Manufacturing of Power Electronics

Faculty Advisor: Juan Rivas-Davila Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Wei Liang Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Jungwon Jungwon Graduate Student Mentor Email: [email protected] Project Description:

The Stanford University Power Electronics Research Laboratory (SUPER-Lab) is developing circuits and components for switching power converters at frequencies greater than 10MHz. This is more than order of magnitude higher than conventional power electronics designs. Among the advantages of this approach are the reductions in size, and the possibility to operate in harsh environments. At this frequencies, it is possible to 3D print many of the passive circuit elements of the power converter. The figure shows some of the components that we have already implemented.

The student(s) working in this project will help us develop 3D models and the plating techniques that can be used to fabricate these devices. Moreover, the student will help us characterized the performance of the devices and how they compare to simulated model, with the goal of obtaining a fully 3D printed power converter. We have had some significant progress in developing these components but there a lot of work to do: selective plating techniques, modeling of optimal core shapes, analysis of thermal properties, etc.

Recommended Courses/Readings: EE101A, EE101B

Desired Qualifications of REU Intern: Electric circuits, basic electro-magnetics, basic semiconductors , familiarity (or willingness to learn) Matlab and Pspice, Comsol.

Maximum number of REU intern positions available: 2 Attachments: 2015REU_3d.pdf (attachment at the end of the document pg. 51-52)



Project #: 7

Project Title: Biomarker Panel Detection Using Electronic Tunneling Spectroscopy

Faculty Advisor: Roger Howe Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Lina Qiu Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Would you like to work in the interdisciplinary field of nanotechnology, biomolecular sensing, and data analytics? Then this project is for you! In recent years, biomolecular detection has been the focus of increasing research, due to its wide application to areas such as disease diagnostics and food and water security. We are developing a new array platform based on tunneling spectroscopy that is inexpensive enough for personal use. Each sensor in the array provides information about the vibrational modes in biomolecules through step-like features in its current-voltage characteristic. The derivative of the tunneling current as a function of sweep voltage includes peaks that reflect the vibration modes, which constitute its signature and can be used for identification.

Using this technique in a real-world sample such as blood serum requires sophisticated pattern recognition algorithms to identify the signature of target molecules in a complex background of proteins. The analytics is challenging, due to the relatively small data set, which results in many machine learning classification techniques not achieving their highest performance. In this REU project, you will be help to develop and advance data analytics for the new platform in order to identify a panel of biomarkers in serum. Depending on your interest and time, you may be involved in designing and conducting experiments with the array, in addition to helping to interpret the information.

Recommended Courses/Readings: See "desired qualifications"

Desired Qualifications of REU Intern: Programming experience in any high-level language is recommended; experience in machine learning, optimization, and signal processing is very desirable, along with an interest in interdisciplinary research in nanotechnology




Project #: 8

Project Title: Cross-Layer Resilience

Faculty Advisor: Subhasish Mitra Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Eric Cheng Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

As systems become increasingly susceptible to soft and hard errors, one potential solution is the use of cross-layer resilience combining resilience techniques from across the system stack to provide low cost solutions. This project will involve implementing and analyzing various resilience techniques to determine their effectiveness and how best to combine diverse techniques across a full system to create solutions with low overhead and high reliability. This work touches on all layers of the system stack, from circuit and architecture all the way up to application software.

Recommended Courses/Readings: EE108A/B, CS106/107

Desired Qualifications of REU Intern: Knowledge of digital design / computer architecture and programming in C/C++.




Project #: 9

Project Title: Validation of Complex Multi-Core System-on-Chips (SoCs)

Faculty Advisor: Subhasish Mitra Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: David Lin Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: [email protected] Project Description:

Computing systems are an indispensible part of all our lives. We depend on powerful portable devices (smartphones, tablets, and notebooks) as well as cloud-based computing servers for communication, e-commerce, finance, entertainment, education, scientific research, public administration, enterprise management, and even socializing. These computing systems are becoming increasingly complex in order to meet performance and energy-efficiency demands. However, such increasing complexities make it very challenging to design computing systems are robust and free of design flaws (bugs). For example, we are already seeing serious design flaws that escape to the field, which can jeopardize correct system operations and cause serious security vulnerabilities. Therefore effective verification and validation techniques are essential to guarantee that future computing systems can continue to provide us with the level of reliability, performance and energy efficiency that we all expect.

Towards this goal, our research group is creating new techniques for effective verification and validation of complex computing systems.

Our group has extensive collaboration with multiple industrial leaders in the area of integrated circuit (IC) design, such as AMD, Freescale, IBM, Intel,

Recently, our group has demonstrated 9 orders of magnitude improvement in error detection capabilities on a commercial multi-core System-on-Chip (SoC) using our Quick Error Detection (QED) technique.

We are looking for students to work on creating new techniques to effectively validate and debug large computing systems. In this project, students will be exposed to the cutting edge of computer architecture, system level programming, circuit design, design emulation, verification and validation techniques, and a variety other topics in electrical engineering and computer science.

Recommended Courses/Readings: EE108, EE282, CS143, CS 140 are desirable, but not required.

Desired Qualifications of REU Intern: Familiarity in a programming language or scripting language.

Familiarity in a hardware description language (Verilog or VHDL).

Some knowledge of computer architecture is desirable.


Project #: 10

Project Title: High Efficiency Nano-Structured Solar Cells Design and Fabrication

Faculty Advisor: James S. Harris Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Yusi Chen Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Jieyang Jieyang Graduate Student Mentor Email: Project Description:

The fast growing solar energy market ($46 billion, 39% annual growth) is at key moment of technique consolidation. Our group is conducting research on the cutting-edge 3rd generation solar cells for low cost and ultra-high efficiency solar energy harvest. In this summer, the student with work on the modeling and design of high efficiency solar cells based on novel theoretical concepts challenging the traditional limit. The student will also have opportunities to conduct experiments on novel solar cell materials and structures and get involved in advanced nanophotonic device fabrication.

The students will work with our solar cell researchers and technicians, and learn the essential device physics from hand-on solar cell design experience. Optimize the cell structure, doping profile, band gap, material composition based fundamental physics models. Grow III-V material for nano-structured solar cell with industry standard epi-taxis equipment.

Recommended Courses/Readings: EE116, E40, EE41

R.F. Pierret, Semiconductor Device Fundamentals, Addison- Wesley

S.J. Fonash, Solar Cell Device Physics (2nd Edition), Elsevier


Maximum number of REU intern positions available: 2 Attachments: Harris Group REU.pdf (attachment at the end of the document pg. 53-86)



Project #: 11

Project Title: High Efficiency Nano-Structured Solar Cells Characterization

Faculty Advisor: James S. Harris Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Yusi Chen Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Jieyang Jieyang Graduate Student Mentor Email: Project Description:

Solar cell market is 46B USD and growing at 39% annually. However, right now, it is the key moment for technique consolidation. We are study the most possible ways for both break fundamental physics limitation as well as economic possible. One of our approach is applying nanoscale light trapping structures on thin film III-V solar cell to retain high efficiency but significant reduce the cost. Our current design has achieved one of the most efficient nanostructured solar cells. Meanwhile, our theoretical studies have found several potential methods to boost the efficiency even higher. We need your help to utilize these ideas and build the high efficiency and low cost solar cells for the future.

The students will work with our solar cell researchers and technicians, and learn solar cell device fabrication and characterization skills. Make their own solar cells with standard fabrication processes in clean room, and characterize it with photocurrent, efficiency measurement, optical measurement, IV curve analysis, etc.

Recommended Courses/Readings: EE116, E40, EE41

R.F. Pierret, Semiconductor Device Fundamentals, Addison- Wesley

S.J. Fonash, Solar Cell Device Physics (2nd Edition), Elsevier





Project #: 12

Project Title: Circuit and Algorithm Designs for a Retinal Prosthesis Calibration System

Faculty Advisor: Subhasish Mitra Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Karthik Ganesan Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

This project will involve design, exploration, and optimization of algorithms and integrated circuits to perform specific calibration functions for retinal prosthesis systems. This project will be in collaboration with other research groups from the Stanford Neurosciences Institute.

Recommended Courses/Readings: -EE 108, EE180, and EE 101 series are bare minimum.

-EE 213, EE 271, EE 214 series, EE 315 series and any neuroscience or algorithms courses would all be a plus

Desired Qualifications of REU Intern: Student should have an interest in working on projects at the intersection of integrated systems design and neuroscience. Knowledge of neuroscience (specifically the visual system) is desirable. Student must absolutely have a very solid background in the




Project #: 13

Project Title: Circumventing Carbon Nanotube Variations by Application-Level Error Compensation

Faculty Advisor: Subhasish Mitra Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Gage Hills Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Carbon nanotube field-effect transistors (CNFETs) are highly promising for improving the energy-efficiency of digital systems by an order of magnitude vs. silicon-CMOS, especially at sub-10 nm technology nodes. However, carbon nanotube (CNT) variations prevent these benefits from being fully realized. While CNT processing advances can potentially overcome CNT variations, such advances have not yet been achieved. Instead, special application-level error compensation techniques circumvent this outstanding challenge, and enable inference-based systems to achieve the energy-efficiency benefits of CNFETs without requiring advanced processing. In this project, we will develop CNFET hardware accelerator cores for machine learning applications that preserve application quality despite CNT variations in CNT processing today.

Recommended Courses/Readings: EE108, EE271, EE116

Desired Qualifications of REU Intern: Experience with semiconductor devices, VLSI circuit design, software & hardware programming and design skills (C++, Verilog, Matlab)




Project #: 14

Project Title: Compression and Denoising of Genomic Data

Faculty Advisor: Tsachy Weissman Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Idoia Ochoa Alvarez Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

One of the highest priorities of modern healthcare research and practice is to identify genomic changes and markers that predispose individuals to debilitating diseases or make them more responsive to certain therapies, which is mainly possible due to a massive generation of sequencing data. This data needs to be stored and analyzed, and thus developing compression schemes that can reduce the storage requirements and facilitate the transmission of the data is of paramount importance.

Lossy compression represents a means to significantly improve compression performance beyond the lossless limit. As one of the purposes of the genomic data under consideration is biological inference, the distortion introduced by the lossy compression may be undesirable in some cases. However, for data that is inherently noisy, like the quality values, lossy compression seems like a natural choice. This statement is based on previous evidence that shows that lossy compression can be used as a means for denoising. That is, lossy compression applied to quality values can potentially reduce the noise presented in the data, yielding a better inference while significantly reducing its size. This would be a breakthrough in how genomic data is treated.

During the summer, we will explore this idea of lossy compression for denoising when applied to genomic data, as well as developing new compression algorithms (lossless and/or lossless) for genomic data.

Recommended Courses/Readings: - Course ee376a: information theory (the part related to compression

- A paper about compression of whole genomes: http://bioinformatics.oxfordjournals.org/content/early/2014/11/21/bioinformatics.btu698.full.pdf+html

A paper on lossy compression of Quality Values: http://www.biomedcentral.com/content/pdf/1471-2105-14-187.pdf

Desired Qualifications of REU Intern: Knowledge of compression, and able to write code on C/C++, Python, Matlab



Attachments:



Project #: 15

Project Title: The Washington, DC, Metro: Does it Change the Global Measures of Geomagnetic Activity?

Faculty Advisor: Antony Fraser-Smith Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Zahra Koochak Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Your professor showed many years ago that BART, the DC-powered mass transit system in San Francisco (SF) was a strong source of low-frequency magnetic field fluctuations throughout the SF Bay area - including at Stanford, which is roughly 40 miles south of the center of BART.

It turns out that the Washington DC Metro is also DC powered and it must be producing similar magnetic field fluctuations all around the Washington area. Unfortunately, the US's official magnetic observatory is located near Fredericksburg, VA, about 50 miles south of the center of Metro, and its measurements are very likely contaminated by Metro.

The Fredericksburg measurements are incorporated into the leading index of global geomagnetic activity, called Kp, and thus this global measure may also be being contaminated by . . hold still . . the economic activity around Washington DC.

Here is what the USGS says about the Fredericksburg observatory (using Google): "Today, because it has produced high-quality data for so many years, Fredericksburg is one of the worlds most important observatories." This study may have an impact on this statement. Your professor has contemplated discussing this Metro problem ONCE AGAIN with the USGS operators but this study will give us something solid to discuss with them. Further, this study may show that Metro has no significant effect on the Kp index, even if it is influencing the Fredericksburg observatory measurements (as seems almost certain).

Both (1) the measurements being made by the Fredericksburg observatory and (2) the Metro system usage data should be available for a statistical analysis - if we decide that is the best way to proceed in this project.

Recommended Courses/Readings: Check http://geomag.usgs.gov/monitoring/observatories/fredericksburg/

and look at the the paper attached below. More recent papers are available but this web site only allows allows one to the attached.


Desired Qualifications of REU Intern: Probably some familiarity with magnetic field measurements at an introductory level is desirable, but most EE students have this. Shouldn't be afraid of looking for correlations between data sets.

Maximum number of REU intern positions available: 2 Attachments: BART Paper 1978.pdf (attachment at the end of the document pg 87-94)



Project #: 16

Project Title: Ge-based Light Source for Optical Interconnects

Faculty Advisor: James Harris Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Xiaochi Chen Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Colleen Colleen Graduate Student Mentor Email: Project Description:

In integrated circuit field, on-chip optical interconnects is a promising solution to the Interconnect Bottleneck. In bio-sensing field, on-chip optical interconnects is used as a solution to label-free, low-cost and high sensitivity optical biosensing. However, one of the biggest challenges of on-chip optical interconnects is making an efficient and CMOS-compatible light source, which theoretically can be achieved by germanium (Ge)-based lasers. In our group, we have successfully demonstrated enhanced light emission from Ge by quantum confinement and strain engineering. This project will focus on investigating Ge surface passivation process in order to further enhance the quantum efficiency of Ge-based light source.

Recommended Courses/Readings: EE116, EE216, EE212





Project #: 17

Project Title: Build Adaptive Neural Networks with Machine Learning Hyperparameter Optimization

Faculty Advisor: Kwabena Boahen Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Sam Fok Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: [email protected] Project Description:

Previously, we've demonstrated that spiking silicon neurons can compute useful functions. Current applications include robot control and Kalman filter state estimation. Although previously constructed networks get the job done, there are still a number of biological neuron "features" and machine learning techniques that we're looking to leverage, namely adaptation, and hyperparameter optimization. Adaptive neurons use feedback to speed up their response while simultaneously decreasing their steady state traffic. Hyperparameter optimization automatically searches the space of learning algorithm parameters to find the best set of parameters.

This project consists of two parts and is open to two students. One part is to simulate adaptive neurons and then create a network of adaptive silicon neurons. The other part is to build and test the hyperparameter optimization module of the neural network compiler.

Recommended Courses/Readings: https://web.stanford.edu/group/brainsinsilicon/documents/Menon-BioRob.pdf

https://web.stanford.edu/group/brainsinsilicon/documents/SNNforBMI_NIPS11.pdf

http://compneuro.uwaterloo.ca/files/publications/eliasmith.2005b.pdf





Project #: 18

Project Title: Stability and Reliability of Highly-stressed Silicon Nitride in Ge-based Electronics and Photonics

Faculty Advisor: James Harris Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Colleen Shang Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

The proposed REU project involves an investigation on the stability of highly-stressed silicon nitride films for applications in electronics, photonics, and optoelectronics. In many of these devices, strain, which is a measure of a materials change in dimensions in response to an applied stress, can have significant influence on the electronic and optical behavior of that material. By using a highly-stressed silicon nitride layer to strain-engineer devices, it is possible to leverage this change in materials properties to achieve the targeted performance.

One cutting-edge example of how silicon nitride layers can be used is in germanium (Ge)-based photonics. Unlike other materials that are used to create lasers, Ge is a poor light-emitting material because of its electronic band structure that gives it an indirect band gap. Fortunately, Ge is well-tailored for strain engineering because it has the potential to become a direct band gap material with vastly improved efficiencies in light-emitting applications like LEDs and lasers. However, the performance of these devices is significantly affected by the stress stability of the silicon nitride layer, making it critical to have an understanding of this stability under various conditions.

The goal of this summer project is to investigate the stress behavior in silicon nitride thin films under conditions such as elevated temperatures and annealing, long periods of storage, and through various etching processes in nanofabrication. This particular project has a well-defined path with many milestones; the number of milestones met is dependent on the enthusiasm of the student researcher. The project will be supervised by a materials science PhD student in a research group with materials characterization and fabrication expertise.

The selected student researcher will gain cleanroom experience on techniques such as plasma-enhanced chemical vapor deposition (PECVD), rapid thermal annealing (RTA), stress measurements, and processes for both wet and dry etching of semiconductor materials. Additionally, there are opportunities to learn material characterization techniques such as atomic force microscopy (AFM) for surface profilometry and scanning electron microscopy (SEM) for structural imaging.

Recommended Courses/Readings: Section 4 in the attachment


Desired Qualifications of REU Intern: We are looking for a motivated undergraduate with a background or interest in the areas of chemistry, physics, materials science, or electrical engineering. Because much of the work is experimental, candidates should be comfortable learning new tools, bei

Maximum number of REU intern positions available: 1 Attachments: Boucaud_Recent advances in Ge emission_Photon Res_2013.pdf (attachment at the end of the document pg. 95-102)



Project #: 19

Project Title: Creating Next Generation Hardware for High-Performance Computing

Faculty Advisor: Mark Horowitz Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Ardavan Pedram Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: [email protected] Project Description:

Help in a research project creating new hardware architectures to support extremely energy efficient high-performance computing. This work will focus on Linear Algebra, Fast Fourier Transform (FFT) and some Machine learning kernels, so some background in linear algebra will be very helpful.

In this project you will work as a member of a team, and focus on one type of application. Based on your background and interest you can focus more on theory, software, or hardware experiments. As part of your work, you will learn about your application kernel. Then you could work on efficiently mapping this kernel on different hardware platforms. You might choose to run your application on our simulator to map such kernels to specialized cores. People with more hardware interests could help writing the RTL code of some of the modules for these kernels. People with interests in evaluation and studying the existing platform could help debug and find corner cases and fix problems in our simulation platform.

Recommended Courses/Readings: Linear Algebra

Programming skills

Computer architecture

Desired Qualifications of REU Intern: Knowledge on

-Linear Algebra

-Computer architecture

At least one of the following programming skills

C++

RTL coding

CUDA

Matlab or Python (Numpy)



Attachments:

Additional Comments: For more info contact Ardavan Pedram:

[email protected]


Project #: 20

Project Title: Large-Scale Model of Human Neocortex

Faculty Advisor: Kwabena Boahen Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Tatiana Engel Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

The dynamics of human neocortex are enormously complex. Yet the local structure of neocortical microcircuits is remarkably stereotyped. Neocortical microcircuits are organized in six horizontal layers defined by their cell types and connectivity. To understand how this local laminar organization supports large-scale cortical dynamics, a detailed biophysical modeling is required. Most existing cortical computer models had to compromise biological details due to limited computational resources. The only large-scale model with detailed local microcircuitry required one minute on a 27 processors cluster to simulate one second of the dynamics [1]. Our lab recently developed neuromorphic simulation platform Neurogrid, which emulates a million neurons in real-time with a high level of biophysical detail. Neurogrid allows us to study large-scale cortical models with required level of laminar resolution and cell-type specificity.

The summer projects primary goal is to build a large-scale model of the human neocortex. The model will emulate in real time dynamics of one million neurons organized in layered microcircuits and weaved in a large-scale network based on human diffusion tensor imaging (DTI) data, similar to model proposed in Ref. [1].

[1] E. M. Izhikevich and G. M. Edelman. Large-scale model of mammalian thalamocortical systems. Proc Natl Acad Sci USA, 105(9):35933598, 2008.

Recommended Courses/Readings: [1] E. M. Izhikevich and G. M. Edelman. Large-scale model of mammalian thalamocortical systems. Proc Natl Acad Sci USA, 105(9):35933598, 2008.

Desired Qualifications of REU Intern: Computational and simulation experience (e.g., Python, Matlab), basic understanding of dynamical systems and differential equations, basic understanding of statistics, interest in the field and eagerness to learn. Experience with neural networks or system


Additional Comments: Please direct any questions about this project to Tatiana Engel via e-mail: [email protected]


Project #: 21

Project Title: Carbon Nanotube Digital Logic VLSI

Faculty Advisor: Subhasish Mitra Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Max Shulaker Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Carbon nanotube (CNT)-based digital systems promise both increased performance and improved energy efficiency beyond the limitations of current silicon-based technologies. Additionally, CNT-based digital systems are naturally suited to enable monolithically-integrated three-dimensional ICs, with interleaving layers of logic and memory. An REU student would work on realizing new monolithically-integrated 3D circuits leveraging CNTs and new memory technologies, such as RRAM or STTRAM. This project would involve simulation and design work, in coordination with nanofabrication, depending on the students interests.

Recommended Courses/Readings: EE116,EE212 (both recommended, not required).





Project #: 22

Project Title: 1D / 2D Nanomaterial Transistor Research

Faculty Advisor: H.S. Philip Wong Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Gregory Pitner Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Research in the characterization of carbon nanotube (CNT) and/or Molybdenum disulfide (MoS2) materials synthesis and transistors for electronic applications. The student will have opportunities to conduct experiments in materials synthesis, materials characterization, or electrical characterization of the fabricated transistors under the supervision of my senior Ph.D. students or researchers. Other topics are also possible by special arrangement. See professor Wong's website.

Recommended Courses/Readings: A strong course background in electrical engineering, applied physics, chemical engineering, or materials science.

Desired Qualifications of REU Intern: Experimental skills in materials characterization and device fabrication is a plus.




Project #: 23

Project Title: Tools for Analyzing Nonlinear Dynamics in Motor Cortical Neural Spiking Activity

Faculty Advisor: Krishna Shenoy Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Jonathan Kao Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Neural spiking activity from motor cortex is inherently dynamical (Shenoy et al., Annual Rev Neurosci 2013). Several studies have analyzed the extent to which simple linear dynamical systems can model such activity (e.g., Churchland et al., 2012). We ask: what are properties of the dynamics when we look at nonlinear projections of the neural activity? To what extent are the dynamics nonlinear? This project will focus on unsupervised machine learning, developing and implementing tools that may be able to help address these questions. For example, the student may implement expectation maximization for nonlinear dynamical systems learning.

Recommended Courses/Readings: It would be good to have a strong grasp of material from EE 263, EE 178 (or equivalent), and CS229. Other relevant courses include EE 278, ENGR 207B, CS 228, EE 378B, EE 364A.

Desired Qualifications of REU Intern: Experience coding in MATLAB.




Project #: 24

Project Title: Fabrication and Characterization of Novel Memory Devices

Faculty Advisor: H.-S. Phillip Wong Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Eric Pop Faculty Advisor #2 Email: [email protected] Graduate Student Mentor: Christopher Neumann Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

As the performance gap between NAND flash and DRAM continues to increase, researchers have become more interested in new storage technologies, often called "storage class memories," to fill it. Our current work focuses on a number of these new technologies, such as phase change memory (PCM), resistive RAM (RRAM), and conductive-bridging RAM (CBRAM), and how we can improve their performance, power usage, and reliability. For this projects, mentees will learn about and assist with the fabrication and measurement of memory devices while learning about how these devices operate and the challenges we currently face.

Recommended Courses/Readings: EE 116 may be useful, but is not required.

Desired Qualifications of REU Intern: Mentees should have an interest in the fabrication, operation, and measurement of semiconductor devices.


Additional Comments: There was a small typo in the previous project description. Please use this one instead.


Project #: 25

Project Title: Feature-Based Analysis of Medical Images

Faculty Advisor: Mark Horowitz Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Blaine Rister Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

The students will help develop exciting new algorithms and software for extracting information from medical images. The goal is to leverage recent developments in feature detection and description to create complete medical imaging applications. Mid-level computer vision and image processing applications include registration, segmentation, classification, and quality assessment of MR and CT scans. Higher-level tasks include automated diagnosis and monitoring of diseases such as multiple sclerosis.

If successful, the investigated technologies could assist health care professionals in analyzing medical images and diagnosing diseases. They could also analyze data from biological experiments, accelerating scientific progress.

Recommended Courses/Readings: Experience in some or all of these areas is helpful, but not required:

Image processing, computer vision, machine learning, medical imaging

Desired Qualifications of REU Intern: Some experience with software development.

Maximum number of REU intern positions available: 2 Attachments: reu_proposal.pdf



Project #: 26

Project Title: Development of Real-Time Linux-Based Embedded x86 System for Neuroelectrophysiology Behavioral Control and Visualization

Faculty Advisor: Krishna Shenoy Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Paul Nuyujukian Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Aim will be to develop a real-time linux-based embedded x86 system for behavioral control of neuroelectrophysiology experiments. Student should be familiar with Linux, python, networking, and legacy computer I/O. Project will involve mostly software development but background in computer hardware and networking is advised. Goal is to develop a multi-computer system that has hard real-time millisecond precision sampling from ethernet, serial, and parallel inputs. System will perform behavioral control logic and data processing and transmit command outputs and data logging via ethernet.

Recommended Courses/Readings: Not required, but suggested.

CS 144, CS 244, CS 344, EE 282, EE 284, EE 384A, CS 1U, CS 107, CS 108, CS 110, CS 140, EE 264, EE 278, CS 229





Project #: 27

Project Title: Architectural Exploration of Emerging Technology-Enabled 3D Monolithic ICs

Faculty Advisor: Subhasish Mitra Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Mohamed Sabry Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: ms Project Description:

The recent energy scaling issues of conventional CMOS have triggered the exploration of alternate technologies overcome this limitation. Both device-level, such as carbon nanotubes and emerging non-volatile memories, and integration-level, such as monolithic 3D integration, provide unique opportunities to provide highly energy-efficiency computing architectures. Such architectures could potential achieve several orders of magnitude energy-delay-product (a metric of energy efficiency) benefits over existing computing architectures.

With recent successful demonstrations (e.g. the CNT computer, and the Stanford high-rise chip), this project aims to provide an architectural and system-level design-space exploration, analysis and optimizations for the projected computing architectures, enabled by the emerging technologies. The project will also take into account the application requirements of the continuously growing BigData application domain.

Desire skills and background:

Computer architecture.

Familiar with existing architectural simulation frameworks, and virtual system simulators.

Good programming and hardware design skills (C, C++, Verilog)

Recommended Courses/Readings:





Project #: 28

Project Title: Device Applications of Novel Atomically Thin Semiconductors

Faculty Advisor: Eric Pop Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Michal Mleczko Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Assisting graduate students in fabrication and characterization of transistors based on novel two-dimensional (2D) semiconductors, grown in collaboration with the Ian Fisher Lab, Dept. of Applied Physics. Mechanical exfoliation and materials characterization of 2D atomically thin layers. These are materials like ZrSe2 and MoTe2, similar to graphene (the subject of the 2010 Nobel prize in physics), but with semiconducting properties. Raman and optical spectroscopy profiling of electronic and structural properties. Electrical probe testing of transistor and other test structures, working closely with graduate student mentors and faculty.

Recommended Courses/Readings: EE 116 recommended. Some introductory quantum mechanics or materials characterization preferable but not required.





Project #: 29

Project Title: Tuning Layered Material Properties by Li Intercalation for Nanoelectronics Applications

Faculty Advisor: Eric Pop Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Feng Xiong Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Layered two-dimensional (2D) semiconductors such as MoS2 are promising candidates for next generation nanoelectronics and optoelectronics applications because of their unique properties. Such materials are available in individual monolayers or in layered stacks, which will be the focus of this investigation. By controllably inserting Li+ ions between the layers, we could achieve reversible tuning of the materials electrical, thermal, optical and thermoelectric properties and thus enhance the device performance. We seek motivated REU students who are interested in nanoelectronics and assisting in the sample fabrication, characterizations and optimizations. Student will learn to characterize 2D materials using atomic force microscopy (AFM) and Raman spectroscopy, as well as perform electrical and optical measurements. This is a unique interdisciplinary project where the student can be involved in both the materials characterization and device optimization.

Recommended Courses/Readings: Interest in nanoelectronics and fabrications. EE116 recommended but not required.





Project #: 30

Project Title: Biomarker Panel Detection Using Electronic Tunneling Spectroscopy Array

Faculty Advisor: Roger Howe Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Lina Qiu Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Chaitanya Chaitanya Graduate Student Mentor Email: Project Description:

Would you like to work in the interdisciplinary field of nanotechnology, biomolecular sensing, and data analytics? Then this project is for you! In recent years, biomolecular detection has been the focus of increasing research, due to its wide application to areas such as disease diagnostics and food and water security. We are developing a new array platform based on tunneling spectroscopy that is inexpensive enough for personal use. Each sensor in the array provides information about the vibrational modes in biomolecules through step-like features in its current-voltage characteristic. The derivative of the tunneling current as a function of sweep voltage includes peaks that reflect the vibration modes, which constitute its signature and can be used for identification.

Using this technique in a real-world sample such as blood serum requires sophisticated pattern recognition algorithms to identify the signature of target molecules in a complex background of proteins. The analytics is challenging, due to the relatively small data set, which results in many machine learning classification techniques not achieving their highest performance. In this REU project, you will be help to develop and advance data analytics for the new platform in order to identify a panel of biomarkers in serum. Depending on your interest and time, you may be involved in designing and conducting experiments with the array, in addition to helping to interpret the information.

Recommended Courses/Readings: Programming experience in any high-level language is recommended; experience in machine learning, optimization, and signal processing is very desirable, along with an interest in interdisciplinary research in nanotechnology.





Project #: 31

Project Title: Development and Integration of Robotic Arm into Brain-Machine Interface System

Faculty Advisor: Krishna Shenoy Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Kwabena Boahen Faculty Advisor #2 Email: [email protected] Graduate Student Mentor: Paul Nuyujukian Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Samir Samir Graduate Student Mentor Email: Project Description:

Aim of project will be to develop and integrate an existing robotic arm into a brain-machine interface system actively underway in a clinical trial. Responsibilities will include programming and controlling robotic arm, developing kinematic and dynnamic models, integrating control system into real-time brain machine interface system

Recommended Courses/Readings: CS 223A, CS 225 CS 226 CS 327 ME 320, CS 144, CS 244, CS 344, EE 282, EE 284, EE 384A, CS 1U, CS 107, CS 108, CS 110, CS 140, EE 264, EE 278, CS 229

Desired Qualifications of REU Intern: Student should have a background in robotics, programming, hardware, and systems integration. Student must be self-motivated and dedicated to working in a complex environment with deadlines and




Project #: 32

Project Title: The High-Throughput Sequencing Revolution

Faculty Advisor: David Tse Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Meisam Rezaviyayn Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Since the sequencing of the first human genome in 2003, the cost of high throughput sequencing have decreased by six orders of magnitude, and the speed of sequencing has increased by a corresponding amount. This has led to a revolution in medicine and biology, and high throughput sequencing has become the modern day microscope to enable not only the study of the genome but many other molecules of biological interest. At the heart of the high throughput sequencing pipeline are large-scale inference and computational problems: hundred of millions of short sub-sequences called reads are generated by the sequencer and they needed to assembled to reconstruct and to estimate the abundances of the long sequences of the underlying molecules of interest. Our group is applying concepts from signal processing, information theory and optimization to design efficient statistical inference algorithms to solve such problems. We are looking for enthusiastic undergraduates to work on various projects, including read error correction, metagenomics assembly and single-cell RNA sequencing.


Desired Qualifications of REU Intern: Programming background. Some knowledge of statistics, probability and algorithms would be desired but not necessary.




Project #: 33

Project Title: Do it Yourself Tiny Neurostimulators

Faculty Advisor: Ada Poon Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Andrew Ma Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Miniaturized electronics, when placed inside the body, can wirelessly monitor and modulate internal activity and thus hold promise as a new class of treatments for disorders. In this project, undergraduate students will learn the design and construction of miniaturized, wirelessly powered neurostimulators that will be implanted in animals for modulating their neural activities.

Recommended Courses/Readings: Basic circuits





Project #: 34

Project Title: Tiny Wireless Camera

Faculty Advisor: Ada Poon Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Stephanie Hsu Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

In this project, undergraduate students will work with graduate students to prototype a millimeter-sized wireless camera. It will be used to image biological activities of freely behaving animals.

Recommended Courses/Readings: FPGA and IO programming





Project #: 35

Project Title: Growth and Optimization of Monolayer Graphene Electronics

Faculty Advisor: Eric Pop Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Ning Wang Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Graphene is a monolayer of carbon atoms, and the subject of the 2010 Nobel prize in physics. The applications of graphene include high-speed electronics, transparent displays, and flexible heat sinks. However, until recently, most research on graphene has unfolded on small, micron-sized pieces. At Stanford, we have recently acquired a system for graphene growth on comparably large-area, 4 substrates. We are seeking an REU student who is interested in nanofabrication and will assist with the growth and electrical optimization of graphene on such large scale substrates for electronic applications. Student will have exposure to cleanroom environments as growths will utilize the new 4 Black Magic CVD Graphene furnace being installed in SNF. Student will be taught to grow monolayer graphene, transfer the graphene from the copper growth substrate to insulating substrates, and to measure the graphene quality via Raman spectroscopy and electrical measurements in a probe-station. This is a unique opportunity to get involved with a one-of-a-kind tool and nanofabrication process being set up at Stanford for the first time.

Recommended Courses/Readings: Basic chemistry and interest in nanofabrication. EE116 recommended but not required.





Project #: 36

Project Title: Nature's Secret Learning Algorithm

Faculty Advisor: Bernard Widrow Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: N/A N/A Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

No one knows how learning actually takes place in living neural networks. That is nature's secret. Nature has available neurons, synapses, and interconnecting wiring. How can these parts be used to create learning in a living neural network? What kind of learning algorithms can be implemented with such parts, and what can be learned with such algorithms? These are pretty fundamental questions, and are the subject of Prof. Widrow's current research. Some ideas toward answering these questions are at hand, but help is needed to go further. Searching the biologic literature to gain a better understanding of how neurons and synapses work and computer simulating their behavior in networks would be a big help.


Desired Qualifications of REU Intern: Some knowledge of biology, chemistry, artificial neural networks and adaptive algorithms would be nice, but this can be learned during the summer.


Additional Comments: Prof. Widrow's initial submission was via email on 1/12/15.


Project #: 37

Project Title: Biomedical Imaging with Microwave Signals

Faculty Advisor: Amin Arbabian Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Hao Nan Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Miaad Miaad Graduate Student Mentor Email: Project Description:

This project uses a hybrid imaging technique for detection of abnormalities in biological tissue. Microwave excitation is combined with ultrasound detection to provide unprecedented access to resolution and contrast deep within the target. Application range from cancer screening to detection of internal injuries.

The summer positions focus on designing several parts of the excitation and receiver electronics as well as the signal processing aspects for reconstruction. Students will learn about basic operation principles of microwave imaging, beamforming, reconstruction techniques as well as various RF components in the transceiver chain. If time allows, parts of the acquisition and processing will be moved to an FPGA receiver board.

Recommended Courses/Readings: Recommended Readings: DSP, FPGA programming, basics of electronics

Recommended Courses: Introductory courses in electronic circuits and signals & systems (EE101A and EE102A or equivalent).

Advanced signal processing courses (e.g. EE264) OR electronic courses (e.g. EE114, EE214, EE251) are a plus.





Project #: 38

Project Title: Online Power Allocation Strategies for Energy Harvesting Wireless Devices

Faculty Advisor: Ayfer Ozgur Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Dor Shaviv Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

The student will design/simulate and compare online power control policies for wireless devices that operate with energy harvested from the natural resources in their environments (ex. solar, wind, thermal etc.).

Recommended Courses/Readings: EE178, EE278

Desired Qualifications of REU Intern: Strong mathematical background, interest in theoretical work




Project #: 39

Project Title: Wireless MRI Receive Arrays

Faculty Advisor: John Pauly Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Greig Scott Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Magnetic Resonance Imaging (MRI) systems use an array of local antennas to pick up the MRI signal. Current systems use from 32 to 128 channels, each with their own signal and power lines. This makes the array complex, heavy, and unreliable. This project will work on building wireless coils, that receive power wirelessly, and communicate the data out wirelessly. Specific projects will depend on student interests and expertise. Possible topics include designing and testing low power, low noise preamps, building prototype dynamic disable switches for detuning the coils during transmit, and designing and building prototype clock synchronization systems.

Recommended Courses/Readings: EE101A and B.

Desired Qualifications of REU Intern: Any additional analog or digital circuit design and fabrication experience would be a plus.




Project #: 40

Project Title: Design and Fabrication of Laser Driven Dielectric Electron Accelerators

Faculty Advisor: Olav Solgaard Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Andrew Ceballos Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

We are developing laser-driven electron accelerators that, due to the higher break down fields in dielectrics than metals, achieve much higher accelerating electric fields than traditional RF accelerators.

The REU summer project we propose is to design, fabricate (partially in the SNF), and test electron accelerators of different geometry, material composition, and surface passivation with the goal of optimizing the break down field of the accelerators. The REU student will work with other students in the group to make accelerators in the SNF. These accelerators will then be tested to determine their breakdown fields and acceleration performance.

Recommended Courses/Readings: See attached paper

Desired Qualifications of REU Intern: Interest in nanofabrication and optics

Maximum number of REU intern positions available: 1 Attachments: Silicon buried grating for electron accelerators.pdf



Project #: 41

Project Title: Analysis of Light Responses in the Retina, and Electrical Stimulation for Design of Artificial Retinas

Faculty Advisor: E.J. Chichilnisky Faculty Advisor Email: [email protected] Faculty Advisor #2 (if applicable): Faculty Advisor #2 Email: Graduate Student Mentor: Nora Brackbill Graduate Student Mentor Email: [email protected] Graduate Student Mentor #2 (if applicable): Graduate Student Mentor Email: Project Description:

Our neurobiology lab uses large-scale multi-electrode recordings to examine how the retina of the eye responds to light, and how we can electrically stimulate retinal neurons to reproduce this activity for the design of artificial retinas to treat blindness. The large data sets obtained by stimulating and recording from hundreds of neurons require a range of data analysis tools and expertise, to efficiently understand the electrical activity that conveys visual information to the brain. We need assistance with a variety of data analysis projects.

Recommended Courses/Readings: http://neurosurgery.stanford.edu/research/chichilnisky/

Desired Qualifications of REU Intern: Matlab, Python and/or other technical programming experience, general interest in neuroscience, working familiarity with UNIX/OSX operating systems.

Maximum number of REU intern positions available: 2 Attachments: N/A

Additional Comments: N/A

Project Title: Innovative Musical Instruments and Systems Faculty Advisor: Robert Dutton ( [email protected] ) Graduate Student Mentor: TBD Project Description: These days everyone loves their hand-held (and smaller!) devices that play music, games, video etc. This is very cool but have you thought about something youve always wanted to be able to do (outputs not available, getting your alarm clock to do more than sing to you, etc.)? This project gives you a quick introduction to some prototyping boards for building new outputs for your favorite (to-be-created) new apps that involve both S/W and H/W. If you go on the web and check out Arduino (or maybe youve already taken Engr 40 M ;) this may help inform you about what is possible. If you want to apply for this project, you need to do that homework and be ready to come and suggest what you would really like your favorite devices to do (that they dont do now). Music apps may be a very compelling opportunity, but its really up to your imagination. And, if you like what you do in this project, you can take EE 122A next fall to do it again and/or continue what youve started here. Recommended Courses: E40M (min. and Required! EE 122A would also be a suitable alternative); value-added, if you have taken EE101A (and maybe B too) Maximum number of REU students (this project): TWO How is this relevant to EE generally and specifically? Basically, Music is the most fun way to see how signal processing has applications and to building cool stuff that includes both hardware (micro-controllers) and wireless systems too. Also, learning Arduion-enabled stuff opens doors for other applicationsfor example robots! This including stuff like quad copters: http://www.youtube.com/watch?v=3CR5y8qZf0Y Music is the introduction to the area but the options from here are endless and Stanfords EE Department is strongly oriented towards more building and learning by building!

This is an example of a Electronic Flute built in a previous REU Project. But, it was wired How about wireless versions of instruments? For example, Air Guitars have often been innovated. Whats your instrument and where would YOU like to take it to the next level?

Project Title: Circuits for Portable Radio Systems Faculty Advisor: Robert Dutton ( [email protected] ) Graduate Student Mentor: TBD Project Description: Radio systems are everywhere. Your phone and many other portable appliances you use daily have radio sub-systems in them. This project will give you a quick introduction to radios and then have you go into the lab and build several simple circuits that are key parts of radio systems. The research goal will be to have you develop both intuition and hands-on experience with radio-frequency (RF) circuits and techniques (i.e. you really need to have some experience from labeither Engr 40 or EE 101A-B preferred but negotiable). The longer-term goals are targeted at novel applications where minimal radio systems can be developed and deployed for things like sensor-based systemsthere are many venues for their application (i.e. sensors in buildings for safety; health-care monitoring systems; traffic control systemsand many more) Recommended Courses / Readings: Engr 40 and/or EE101A-B Maximum number of REU students (this project): TWO

Schematic of a Two-Quadrant Multiplier for Demodulation of AM

The Board-Level Implementation

Project Title: Filter PCB structures for high frequency power convertersFaculty Advisor: Juan Rivas-DavilaFaculty Advisor Email: [email protected] Student Mentor: Lei GuGraduate Student Mentor Email: [email protected] Student Mentor: Wei LiangGraduate Student Mentor Email: [email protected]

BackgroundA significant part of the cost and volume of modern electronic equipment is due to the energy-conversion and energy-storage systems that they require. A challenge of particular importance, and the subject of this project, is the miniaturization of power electronic circuits. Miniaturization of these systems is difficult because the power conversion process requires passive elements with significant energy storage. Thus, design and manufacturing methods that reduce energy storage requirements are very valuable in reducing the size of power converters. Power inductors and transformers, in particular, are challenging to miniaturize because of their poor performance when scaled down in size, and the difficulty of fabricating them with available planar processes.

A family of approximating networks for transmission lines, the focus of this project, enables miniaturization by internally circulating energy and exchanging delay fidelity for bulk energy storage. These multi-resonant components are substantially smaller than their lumped counterparts, in particular requiring less inductance, and enforce useful waveform symmetries that can be traded for higher power or higher efficiency. Lumped analogs of transmission lines, and delay-based means of processing energy in general, exploit rather than fight the parasitics which can restrict conventional designs to lower switching frequencies, and are compatible with RF power-conversion techniques

Position description

The student will model, simulate and fabricate an input/output filter using Printed Circuit Board components which can emulate the impedance characteristics of a shorted quarter wavelength transmission line for a 10s of MHz resonant converter. The resonant frequencies would be aligned with switching frequency of the converter. If the fundamental switching frequency component and odd harmonic components can be aligned with poles of the input impedance of this filter and the even harmonic components can be aligned with zeros of the input filter, the current and voltage waveforms in the converter will be symmetrical. Furthermore, if we could design the input impedances at different harmonic components in a way that we want, in this case the voltage and current waveforms could also be formed by the input filter before it reaches the core converter.

The input impedance of a quarter-wavelength transmission line looks like this:

A lumped circuit network that can realize the described impedance is:

It is hard to physically and expensive to solder many Surface-Mount package inductors and capacitors on the Printed Circuit Board (PCB). However, it is possible to just fabricate the LC components directly on the PCB, as shown in the figure. In this sense, we can fabricate an input filter which can emulate the input impedance of a shorted quarter-wavelength transmission for a certain switching frequency using PCB fabrication technology. This will make the massive fabrication very easy and accurate. Also, it will also benefit the design of a resonant converter with a certain switching frequency. A PCB structure with the desired electrical characteristic will look something like this.

The student will first design the dimensions of the PCB multi resonant input filter by hand calculations. Then the student will try to verify the design in circuit simulation tools like LTspice. The student will also need to make such design of a certain spec input filter scriptable in PCB design tools, like Eagle. So that a standard design script of such a filter can be provided for any universal design. Finally the student will also implement the input filter on PCB and experimentally verify the design flow. The student can explore the design flow by himself/herself.

Prerequisites: EE101-A, EE101-B

Project Title: 3D modeling and manufacturing of power electronics componentsFaculty Advisor: Juan Rivas-DavilaFaculty Advisor Email: [email protected] Student Mentor: Wei LiangGraduate Student Mentor Email: [email protected] Student Mentor #2 (if applicable): Junwong ChoiGraduate Student Mentor Email: [email protected]

Project Description:The Stanford University Power Electronics Research Laboratory (SUPER-Lab) is developing circuitsand components for switching power converters at frequencies greater than 10MHz. This is more than order of magnitude higher than conventional power electronics designs. Among the advantages of this approach are the reductions in size, and the possibility to operate in harsh environments. At this frequencies, it is possible to 3D print many of the passive circuit elements of the power converter. The figure shows some of the components that we have already implemented.

The student(s) working in this project will help us develop 3D models and the plating techniques that can be used to fabricate these devices. Moreover, the student will help us characterized the performance of the devices and how they compare to simulated model, with the goal of obtaining a fully 3D printed power converter. We have had some significant progress in developing these components but there a lot of work to do: selective plating techniques, modeling of optimal core shapes, analysis of thermal properties, etc.

Recommended Courses/Readings: EE101A, EE101BDesired Qualifications of REU Intern: Electric circuits, basic electro-magnetics, basic semiconductors , familiarity (or willingness to learn) Matlab and Pspice, Comsol.

Illustration 1: Preliminary implementation of a converter with3D printer components

1Nanostructures in III-V solar cells

Yangsen Kang, Yusi Chen, Jieyang Jia, Yijie Huo,

Prof. James Harris

March 11th 2013

2Our Team

Core team:

Alumni:

2 Ph.D. students, 5 REUs

Collaborators:

Prof. Yi Cui Prof. Shanhui Fan Prof. Mark Brongersma

Prof. Paul McIntyre Prof. Philip Wong

OEpic Corp. SolarJunction Corp.

Yijie Huo Yangsen Kang Jieyang JiaYusi Chen Linfei Gao

33

World Energy Challenge

Source: John F. Bookout,Two Centuries of Fossil Fuel Energy International Geological

REU 2015 Project Descriptions_Final

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Transcript of REU 2015 Project Descriptions_Final