2017

Undergrad Vivian Wang (BS '17) is a 2017 Churchill Scholarship winner
March 2017

Congratulations to Vivian Wang (BS '17) on her well-deserved award!

As an undergraduate, Vivian has been involved in numerous events on campus. She is a former co-director, and teacher, of Stanford Splash, which brings middle and high school students to campus to learn from Stanford students. Vivian has taught Splash courses since 2014. Her most recent course was "Sewable Electronics."

Vivian has also been a teaching assistant for two of the department's most popular courses, "An Intro to Making: What is EE" and "Digital Systems Design". She was selected through a competitive process, to be a peer tutor in math and physics. Vivian also participated in EE's REU program, doing research, and eventually co-authoring a paper with Professor Jim Harris. Vivian has also worked as an undergraduate research assistant for Professor Amin Arbabian.

"I am grateful for the research and other experiences Stanford has provided me thus far and look forward to the scientific and cultural opportunities provided through the Churchill Scholarship," Wang said.

The goal of the Churchill Scholarships program, established at the request of Sir Winston Churchill, is to advance science and technology on both sides of the Atlantic, helping to ensure future prosperity and security.

 

Excerpted from Stanford News article, "Stanford electrical engineering senior wins Churchill Scholarship"

March 2017

Professor Shanhui Fan has been selected to receive the 2017 Vannevar Bush Faculty Fellowship.

"The fellowship program provides research awards to top-tier researchers from U.S. universities to conduct revolutionary "high risk, high pay-off" research of strategic importance to the Department of Defense," said Mary J. Miller, acting assistant secretary of defense for research and engineering.

Fellows conduct basic research in core science and engineering disciplines that underpin future DoD technologies, such as, quantum information science, neuroscience, nanoscience, novel engineered materials, applied mathematics, statistics, and fluid dynamics. Fellows directly engage with the DoD research enterprise to share knowledge and insights with DoD civilian and military leaders, researchers in DoD laboratories, and the national security science and engineering community.

"Grants supporting the program engage the next generation of outstanding scientists and engineers in the "hard" problems that DoD needs to solve," Miller said.

DoD congratulates each of these remarkable scientists and engineers on selection as Vannevar Bush Faculty Fellows, bringing the current cohort to 45 Fellows.

 

Please join the department in congratulating Shanhui for this well deserved recognition and support of his outstanding research!

 

DoD press release

EE PhD admit poster session winners
March 2017

The 2017 EE Admit Weekend welcomed nearly 80 newly admitted PhD graduate students. The 2-day event connected admitted students with current students, faculty and staff. Bringing everyone together, the event encourages exploration of the department and current research. Friday concludes with a PhD student research poster session and social reception. Admits enthusiastically engaged with presenters to discuss their research and various aspects of graduate life at Stanford.

The research posters present topics from EE's core research areas. In addition to meeting incoming PhD students, it is an opportunity for current grad students to present their work and hone their presentation skills. The posters are competitively judged, based on oral presentation, visual quality, and clarity of presenting their research within a one minute timeframe. The judges include staff, faculty and students. A winner from each core research area are selected based on their score in the judging criteria.

The well-deserved awards went to:

  • Leighton Barnes (pictured below second from left) winner in Information Systems and Science for poster titled "Geometry and the Relay Channel,” 
  • Adrian Alabi (pictured below, center) in Hardware/Software Systems for poster titled "915 MHz FSK Detection for Wireless Ultrasonic Imaging Data Reception,” 
  • Max Wang (pictured below second from right) in Physical Technology and Science for poster titled "Minimally Invasive Ultrasonically Powered Implants for Next-Generation Therapies and Neuromodulation” 

The winning researchers were awarded a gift card and certificate, presented by Daisy Chavez (pictured below, left), Graduate Admissions Specialist and Student Life Coordinator and Professor Andrea Goldsmith (pictured below, right), Chair of the EE Student Life Committee.

Congratulations and thanks to everyone for participating in the 2017 EE Admit Weekend and research poster session. Additional thanks to the EE Admissions, GSEE, and the EE Student Life Committee for sponsoring the poster contest and generous prizes.

March 2017

Kwabena Boahen's research on building brain-like computers, or neuromorphic computers, is moving toward creating physical devices that are more energy efficient and robust. Kwabena envisions this technology would be most useful in embedded systems that have extremely tight energy requirements, such as very low-power neural implants or on-board computers in autonomous drones.

While others have built brain-inspired computers, he and his collaborators have developed a five-point prospectus for how to build neuromorphic computers that directly mimic in silicon what the brain does in flesh and blood.

The first two points of the prospectus concern neurons themselves, which unlike computers operate in a mix of digital and analog mode. In their digital mode, neurons send discrete, all-or-nothing signals in the form of electrical spikes, akin to the ones and zeros of digital computers. But they process incoming signals by adding them all up and firing only once a threshold is reached – more akin to a dial than a switch.

That observation led Kwabena to try using transistors in a mixed digital-analog mode. Doing so, it turns out, makes chips both more energy efficient and more robust when the components do fail, as about 4 percent of the smallest transistors are expected to do.

From there, Kwabena builds on neurons' hierarchical organization, distributed computation and feedback loops to create a vision of an even more energy efficient, powerful and robust neuromorphic computer.

Over the last 30 years, Kwabena's lab has actually implemented most of their ideas in physical devices, including Neurogrid, one of the first truly neuromorphic computers. In another two or three years, Boahen said, he expects they will have designed and built computers implementing all of the prospectus's five points.

He states that neuromorphic computers will not replace current computers. The two are complementary.

An additional challenge is getting others, especially chip manufacturers, on board. Kwabena is not the only one thinking about what to do about the end of Moore's law or looking to the brain for ideas. IBM's TrueNorth, for example, takes cues from neural networks to produce a radically more efficient computer architecture. On the other hand, it remains fully digital, and, Kwabena said, 20 times less efficient than Neurogrid would be had it been built with TrueNorth's 28-nanometer transistors.

Professor Kwabena Boahen is also a member of Stanford SystemX and the Stanford Computer Forum. His work was supported by a Director's Pioneer Award and a Transformative Research Award from the U.S. National Institutes of Health and a Long Range Science and Technology Grant from the U.S. Office of Naval Research.

 

Below, Professor Kwabena Boahen shares his research with Electrical Engineering undergraduates who are in the REU program (Research Experience for Undergrads).

Boahen shares his research with EE undergrads who are in the REU program

 


 

Excerpted from Stanford News, "As Moore's law nears its physical limits, a new generation of brain-like computers comes of age in a Stanford lab"

Image credit (top): Linda A. Cicero / Stanford News Service

 

February 2017

At the invitation of the Optical Society of America (OSA), Professor Jelena Vuckovic hosted a Reddit Science Ask Me Anything (AMA) session. The session was primarily directed to students, however anyone can post a question.

The Reddit Science community (known as /r/Science) has created an independent, science-focused AMA Series – the Science AMA Series. Their goal is to encourage discussion and facilitate outreach while helping to bridge the gap between practicing scientists and the general public. This series is open to any practicing research scientist, or group of scientists, that wants to have a candid conversation with the large and diverse Reddit Science community.

Reddit's AMA format introduced Jelena and her research in nanophotonics, quantum optics, nonlinear optics, quantum information technologies, and optoelectronics. She received about 25 questions and provided answers in the course of an afternoon.

The thread is available on Reddit, and can be viewed at www.reddit.com/r/science/comments/5ssbx2/science_ama_series_im_dr_jelena_vuckovic/

'AMA' is short for 'Ask Me Anything,' and was created by the Reddit community as an opportunity for interesting individuals to field questions about anything and everything. AMAs hosted on Reddit have become an exciting platform for people to have direct discussions and gain insight into the lives of unique individuals. Some of the historically most-popular AMAs include those from President Barack Obama, Sir David Attenborough, Bill Gates, Elon Musk and many others.

Related:

 

 

Additional Sources, Reddit.com

 

March 2017

Kristen Lurie (PhD '16) and Audrey Bowden authored a paper published in Biomedical Optics Express that presents a computational method to reconstruct and visualize a 3D model of organs from an endoscopic video that captures the shape and surface appearance of the organ.

Although the team developed the technique for the bladder, it could be applied to other hollow organs where doctors routinely perform endoscopy, including the stomach or colon.

"We were the first group to achieve complete 3D bladder models using standard clinical equipment, which makes this research ripe for rapid translation to clinical practice," states Kristen Lurie (EE PhD, '16), lead author on the paper.

"The beauty of this project is that we can take data that doctors are already collecting," states Audrey.

One of the technique's advantages is that doctors don't have to buy new hardware or modify their techniques significantly. Through the use of advanced computer vision algorithms, the team reconstructed the shape and internal appearance of a bladder using the video footage from a routine cystoscopy, which would ordinarily have been discarded or not recorded in the first place.

"In endoscopy, we generate a lot of data, but currently they're just tossed away," said Joseph Liao, professor of Urology and co-author. According to Liao, these three-dimensional images could help doctors prepare for surgery. Lesions, tumors and scars in the bladder are hard to find, both initially and during surgery.

This technique is the first of its kind and still has room for improvement, the researchers said. Primarily, the three-dimensional models tend to flatten out bumps on the bladder wall, including tumors. With the model alone, this may make tumors harder to spot. The team is now working to advance the realism, in shape and detail, of the models.

Future directions, according to the researchers, include using the algorithm for disease and cancer monitoring within the bladder over time to detect subtle changes, as well as combining it with other imaging technologies.

 

Read Paper

 

 

Excerpted from Stanford News, "Stanford scientists create three-dimensional bladder reconstruction"

 

March 2017

The goal of Eric Pop's team was to develop a manufacturing process to turn single-layer chips into practical realities.

The first atomically thin material was measured in 2004 when scientists observed that graphene – a material related to the "lead" in pencils – could be isolated in layers the thickness of a single carbon atom. The scientists who made this finding shared the 2010 Nobel Prize in Physics.

But the process used to make that discovery – the scientists lifted layers of graphene off a rock using sticky tape – was of no use in turning ultrathin crystals into next-generation electronics.

In the wake of the graphene discovery, engineers embarked on a quest to find similar materials and, more importantly, practical ways to fashion atomically thin switches into circuits.

It is on the issue of manufacturability where Pop's team made a big advance. They started with a single layer of material called molybdenum disulfide. The name describes its sandwich-like structure: a sheet of molybdenum atoms between two layers of sulfur. Previous research had shown that molybdenum disulfide made a good switch, controlling electricity to create digital ones and zeroes.

The question was whether the team could manufacture a molybdenum disulfide crystal big enough to form a chip. That requires building a crystal roughly the size of your thumbnail. This may not sound like a big deal until you consider the aspect ratio of the crystal required: a chip just three atoms thick but the size of your thumbnail is like a single sheet of paper big enough to cover the entire Stanford campus.

The Stanford team manufactured that sheet by depositing three layers of atoms into a crystalline structure 25 million times wider than it is thick. Smithe achieved this by making ingenious refinements to a manufacturing process called chemical vapor deposition. This approach essentially incinerates small amounts of sulfur and molybdenum until the atoms vaporize like soot. The atoms then deposit as an ultra-thin crystalline layer on a "handle" substrate, which can be glass or even silicon.

However, the researchers' job was not done. They still had to pattern the material into electrical switches and to understand their operation. For this, they made use of a recent advance led by English, who discovered that extremely clean deposition conditions are essential to form good metallic contacts with the molybdenum disulfide layers. The wealth of new experimental data available now in the lab has also enabled Suryavanshi to craft accurate computer models of the new materials and to begin predicting their collective behavior as circuit components.

"We have a lot of work ahead to scale this process into circuits with larger scales and better performance," Pop said. "But we now have all the building blocks."

 

 

Excerpted from Stanford School of Engineering news, "A team of engineers create a prototype chip a mere three atoms thick"

Lisa Sickorez receives 2016 SoE Leadership Award
March 2017

 

Lisa Sickorez received the School of Engineering's Leadership Award. Lisa's award underscores her dedication and enthusiasm for advancing the mission of the school and Stanford university.  

The Leadership Award recognizes mentoring and management contributions. Her citation reads, "Lisa is known as an individual who leads by example, finds solutions to difficult problems, and has the integrity to do the right thing. In addition to managing a demanding workload in the EE department, Lisa stepped up to assist the SoE Finance team with implementing a new budgeting system. Her business knowledge and expertise is a great resource to the EE department and to all departments in the school."

Lisa was presented with her award at an annual Staff Service Awards Ceremony in March.

 

Hearty congratulations to Lisa on receiving the 2016 School of Engineering Leadership Award.

March 2017

Congratulations to Isha Datye and Alexander Gabourie on their winning poster, "Reduction of hysteresis in MoS2 transistors using pulsed voltage measurements". 

 

The Device Research Conference (DRC) brings together leading scientists, researchers, and students to share their latest discoveries in device science, technology and modeling. 2016 marked the 75th anniversary of the DRC — the longest running device research meeting in the world.

 

Abstract

Transistors based on atomically thin two-dimensional (2D) materials like MoS2 have attractive properties for applications in low-power electronics. However, in practice their electrical measurements often exhibit hysteresis, masking their intrinsic behavior. In this study we used pulsed measurements to decrease hysteresis, examine charge trapping, and extract device parameters (like mobility) that represent the "true" behavior of 2D devices. Hysteresis is minimized even with modest ≤ 1 ms pulses, and the extracted mobility converges to a unique value, unlike the less reliable conventional methods which rely either on forward or reverse DC sweeps.

Link to paper

March 2017

Ning Wang, EE PhD candidate, received best paper and best poster awards at TECHCON 2016. The title of his paper is "GDOT: A Graphene-Based Nanofunction for Dot-Product Computation".

TECHCON is a technical conference and networking event for Semiconductor Research Corporation (SRC) members and students.

Ning Wang's research is in Physical Technology & Science and his advisor is Eric Pop.

 

Congratulations to Ning on his well-deserved recognition!

 

Abstract
Though much excitement surrounds two-dimensional (2D) beyond CMOS fabrics like graphene and MoS2, most efforts have focused on individual devices, with few high-level implementations. Here we present the first graphene-based dot-product nanofunction (GDOT) using a mixed-signal architecture. Dot product kernels are essential for emerging image processing and neuromorphic computing applications, where energy efficiency is prioritized. SPICE simulations of GDOT implementing a Gaussian blur show up to ~10(4) greater signal-to-noise ratio (SNR) over CMOS based implementations - a direct result of higher graphene mobility in a circuit tolerant to low on/off ratios. Energy consumption is nearly equivalent, implying the GDOT can operate faster at higher SNR than CMOS counterparts while preserving energy benefits over digital implementations. We implement a prototype 2-input GDOT on a waferscale 4" process, with measured results confirming dot-product operation and lower than expected computation error.

 

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