EE Student Information

student

prof Krishna Saraswat
January 2022

Co-lead authors Koosha Nassiri Nazif and Alwin Daus, both EE postdoctoral scholars, describe their tungsten diselenide solar cells that boast a power-per-weight ratio on par with established thin-film solar cell technologies in their recently published paper. Their prototype achieves 5.1 percent power conversion efficiency, and the team projects they could practically reach 27 percent efficiency upon optical and electrical optimizations. That figure would be on par with the best solar panels on the market today, silicon included.

Their prototype realized a 100-times greater power-to-weight ratio of any transition metal dichalcogenides (TMDs) yet developed. That ratio is important for mobile applications, like drones, electric vehicles and the ability to charge expeditionary equipment on the move. When looking at the specific power – a measure of electrical power output per unit weight of the solar cell – the prototype produced 4.4 watts per gram, a figure competitive with other current-day thin-film solar cells, including other experimental prototypes.

"We think we can increase this crucial ratio another ten times through optimization," states Krishna, adding that they estimate the practical limit of their TMD cells to be a remarkable 46 watts per gram.


Pictured below are Professor Krishna Saraswat (left) and Dr. Koosha Nassiri Nazif (right), and a photograph of WSe2 solar cells on a flexible polyimide substrate held up with a pair of tweezers. Photo credit: Dr. Koosha Nassiri Nazif.

Prof. Krishna Saraswat and Dr. Koosha Nassiri Nazif

"Imagine an autonomous drone that powers itself with a solar array atop its wing that is 15 times thinner than a piece of paper," said Koosha Nassiri Nazif, a doctoral scholar in EE. "That is the promise of TMDs."

This is collaborative work between the research groups of Professor Krishna Saraswat and Professor Eric Pop.
Additional authors include

  • Department of Electrical Engineering: Koosha Nassiri Nazif, Alwin Daus, Sam Vaziri, Aravindh Kumar, Frederick Nitta, Siavash Kananian, Raisul Islam, Prof. Ada S. Y. Poon, Prof. Eric Pop & Prof. Krishna C. Saraswat
  • Geballe Laboratory for Advanced Materials (GLAM): Jiho Hong, Nayeun Lee & Prof. Mark L. Brongersma
  • Department of Materials Science and Engineering: Jiho Hong, Nayeun Lee, Michelle E. Chen, Prof. Mark L. Brongersma, Prof. Eric Pop & Prof. Krishna C. Saraswat
  • Department of Electrical and Computer Engineering, Sungkyunkwan University: Kwan-Ho Kim & Jin-Hong Park
  • Department of Electrical and Systems Engineering, University of Pennsylvania: Kwan-Ho Kim
  • SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University: Jin-Hong Park
  • Department of Applied Physics: Prof. Mark L. Brongersma

Related Sources

prof Jelena Vučković
January 2022

Excerpted from "Stanford engineers and physicists study quantum characteristics of 'combs' of light"

Professor & Chair Jelena Vučković states, "Many groups have demonstrated on-chip frequency combs in a variety of materials, including recently in silicon carbide by our team. However, until now, the quantum optical properties of frequency combs have been elusive. We wanted to leverage the quantum optics background of our group to study the quantum properties of the soliton microcomb."


While soliton microcombs have been made in other labs, the Stanford researchers are among the first to investigate the system's quantum optical properties, using a process that they outline in a paper published Dec. 16 in Nature Photonics. When created in pairs, microcomb solitons are thought to exhibit entanglement – a relationship between particles that allows them to influence each other even at incredible distances, which underpins our understanding of quantum physics and is the basis of all proposed quantum technologies. Most of the "classical" light we encounter on a daily basis does not exhibit entanglement.

"This is one of the first demonstrations that this miniaturized frequency comb can generate interesting quantum light – non-classical light – on a chip," said Kiyoul Yang, a research scientist in Vučković's Nanoscale and Quantum Photonics Lab and co-author of the paper. "That can open a new pathway toward broader explorations of quantum light using the frequency comb and photonic integrated circuits for large-scale experiments."

Proving the utility of their tool, the researchers also provided convincing evidence of quantum entanglement within the soliton microcomb, which has been theorized and assumed but has yet to be proven by any existing studies.

"I would really like to see solitons become useful for quantum computing because it's a highly studied system," said Melissa Guidry, a graduate student in the Nanoscale and Quantum Photonics Lab and co-author of the paper. "We have a lot of technology at this point for generating solitons on chips at low power, so it would be exciting to be able to take that and show that you have entanglement."

 

Read full article: Stanford News, "Stanford engineers and physicists study quantum characteristics of 'combs' of light

 

Related News

Prof. Eric pop
September 2021

Professor Eric Pop's lab - Pop Lab - took a long shot adapting phase-change memory (PCM) to plastic substrates – turns out the energy-efficiency significantly improved compared to PCM on conventional silicon substrates.

Phase change materials leverage changes in structure into differences in electrical resistance that are attractive for computer memory and processing applications. Khan et al. developed a flexible phase change memory device with layers of antimony telluride and germanium telluride deposited directly on a flexible polyimide substrate. The device shows multilevel operation with low switching current density. The combination of phase change and flexible mechanical properties is attractive for the large number of emerging applications for flexible electronics.

"It's the same atoms as conventional phase-change memory but in beautiful striped alternating thin layers, also known as a superlattice," says Professor Eric Pop.

Eric's group put arrays of memory cells made of superlattices of alternating layers of antimony telluride and germanium telluride on flexible plastic substrates. They were curious whether they could make it work—flexible memory is a key enabling technology for electronic skin, lightweight environmental sensors, and other unconventional electronics. Once grad student Asir Intisar Khan and postdoc Alwin Daus figured out how to make these devices at temperatures that would not melt the polyimide substrate, the researchers were surprised by what they found.

"The flexible substrate provides an extra advantage we did not anticipate," reports Eric Pop. The current density required to switch the flexible memory cells is 10 to 100x lower than any previously reported phase-change memory, and the memory cells maintain their performance when the substrate is bent. After seeing the results, the team was "scrambling," he continues. "Why is this better?" The Stanford group believes that the layers in the superlattice, the cell's "pore-like" design, as well as the insulating properties of the plastic substrate, help confine the energy applied to the memory cells, making them heat up more efficiently and spurring a phase change at lower electrical currents.

Excerpted from c&en (Chemical & Engineering News), "Flexible memory uses less power" and IEEE Spectrum, "This Memory Tech Is Better When It's Bendy

 

 Related

image of professor Eric Pop
June 2021

Professor Eric Pop and team describe the ability to produce nanoscale flexible electronics In their paper, "High-Performance Flexible Nanoscale Transistors Based on Transition Metal Dichalcogenides," published in Nature Electronics. Flexible electronics promise bendable, shapeable, yet energy-efficient computer circuits that can be worn on or implanted in the human body to perform myriad health-related tasks. Future variations future of the circuits will communicate wirelessly with the outside world – another large leap toward viability for flextronics, particularly those implanted in the human body or integrated deep within other devices connected to the internet of things.

[...]
With a prototype and patent application complete, postdoc Alwin Daus and Professor Eric Pop have moved on to their next challenges of refining the devices. They have built similar transistors using two other atomically thin semiconductors (MoSe2 and WSe2) to demonstrate the broad applicability of the technique.

Meanwhile, Alwin said that he is looking into integrating radio circuitry with the devices, which will allow future variations to communicate wirelessly with the outside world – another large leap toward viability for flextronics, particularly those implanted in the human body or integrated deep within other devices connected to the internet of things.

Eric reports, "This is more than a promising production technique. We've achieved flexibility, density, high performance and low power – all at the same time. This work will hopefully move the technology forward on several levels."

Co-authors include postdoctoral scholars Sam Vaziri and Kevin Brenner, EE doctoral candidates Victoria Chen, Çağıl Köroğlu, Ryan Grady, Connor Bailey and Kirstin Schauble, and research scientist Hye Ryoung Lee. Pop Lab People

 

Excerpted from "Stanford researchers develop new manufacturing technique for flexible electronics" Stanford News

June 2021
Capstone Course Award

Design Award, Capstone Courses

This award is given to the outstanding design project in one of the capstone design courses. This year the award goes to Ryan Ressmeyer for his EE 264 project: "Orthogonal Frequency Division Multiplexer".

Centennial Award

Centennial Teaching Award

The School of Engineering's Centennial TA Award is given to students to recognize their outstanding contributions to teaching. The 2021 Centennial TA award recipients are: Shubham ChandakAmy FritzSiavash KananianAnna NunesErik Van.

JEDI award

Justice, Equity, Diversity & Inclusion (JEDI) Graduation Awards

The School of Engineering's Justice, Equity, Diversity & Inclusion Graduation Awards recognizes the exceptional work done by graduating graduate students in outreach and mentorship for underserved and underrepresented communities with the goal of improving the accessibility of STEMM. This includes the fields of Science, Technology, Engineering, Math, and Medicine.
The JEDI Graduation Award recipients are Crystal NattooCindy NguyenSean Peters.

Gibbons Award

James F. Gibbons Outstanding Student Teaching Award 2021

The James F. Gibbons Award for Outstanding Student Teaching Award highlights students who have been nominated by faculty and peers for their extraordinary service as teaching assistants. Thank you for your tremendous work in our department – Anna NunesElizabeth Chen, and Erik Van.

Phil Levis Chairs Award 2021

Chair's Award for Outstanding Contributions to Undergraduate Education

Congratulations to Professor Phil Levis - Over the last few years, Phil has led a team of students on the FLIGHT project, it's a large-scale electromechanical art installation for the Packard [building] stairwell, right behind me. It consists of 76 Fractal Flyers, each of which is a programmable shape inspired by the geometry of the stairwell, with hundreds of LEDs and moving dichroic surfaces that cast colored reflections and shadows.

Terman Award

Frederick E. Terman Engineering Scholastic Award

The Terman Award is presented to the top 5% of each senior class in the School of Engineering. We are pleased to congratulate our 2021 Terman Scholars for their outstanding work. Joaquin BorggioRahul Lall, and Ryan Ressmeyer.

TBP Teaching Award

Tau Beta Pi (TBP) Teaching Honor Roll (The Engineering Society)

Professor John Pauly and Assistant Professor Mary Wootters - This award recognizes outstanding faculty instructors in the School of Engineering. These faculty instructors were nominated by Stanford students to recognize their distinguished teaching, superior mentorship, and/or any other notable contribution to engineering education at Stanford.

TBP Student Honor Roll

Tau Beta Pi (TBP) Honor Roll (The Engineering Society)

The California Gamma chapter of Tau Beta Pi at Stanford University serves the Stanford community by acting as a representative entity for academic excellence, leadership, and continued service to our community. Tau Beta Pi is the only engineering honor society representing the entire engineering profession. Congratulations to
  • Vineet Edupuganti, EE-BS
  • Yong (Collin) Kwon, EE-BS
  • Rahul Lall, EE-BSH
  • Michael Oduoza, EE-BS
  • Kangrui Xue, EE-BS

image of PhD candidate Riley Culberg
April 2021

Research by EE PhD candidate Riley Culberg and Prof. Dustin Schroeder is revealing the long-term impact of vast ice melt in the Arctic.

Using a new approach to ice-penetrating radar data, researchers show that this melting left behind a contiguous layer of refrozen ice inside the snowpack, including near the middle of the ice sheet where surface melting is usually minimal. Most importantly, the formation of the melt layer changed the ice sheet's behavior by reducing its ability to store future meltwater. The research appears in Nature Communications.

"When you have these extreme, one-off melt years, it's not just adding more to Greenland's contribution to sea-level rise in that year – it's also creating these persistent structural changes in the ice sheet itself," said lead author Riley Culberg, EE PhD candidate. "This continental-scale picture helps us understand what kind of melt and snow conditions allowed this layer to form."

Airborne radar data, a major expansion to single-site field observations on the icy poles, is typically used to study the bottom of the ice sheet. But by pushing past technical and computational limitations through advanced modeling, the team was able to reanalyze radar data collected by flights from NASA's Operation IceBridge from 2012 to 2017 to interpret melt near the surface of the ice sheet, at a depth up to about 50 feet.

"Once those challenges were overcome, all of a sudden, we started seeing meltwater ice layers near the surface of the ice sheet," EE courtesy professor, Dustin Schroeder said. "It turns out we've been building records that, as a community, we didn't fully realize we were making."

Melting ice sheets and glaciers are the biggest contributors to sea-level rise – and the most complex elements to incorporate into climate model projections. Ice sheet regions that haven't experienced extreme melt can store meltwater in the upper 150 feet, thereby preventing it from flowing into the ocean. A melt layer like the one from 2012 can reduce the storage capacity to about 15 feet in some parts of the Greenland Ice Sheet, according to the research.

 

 

Excerpted from "Stanford researchers reveal the long-term impacts of extreme melt on Greenland Ice Sheet", Stanford News, April 20, 2021

image of prof James Zou and PhD Amirata Ghorbani
February 2021

Each of us continuously generates a stream of data. When we buy a coffee, watch a romcom or action movie, or visit the gym or the doctor's office (tracked by our phones), we hand over our data to companies that hope to make money from that information – either by using it to train an AI system to predict our future behavior or by selling it to others.

But what is that data worth?

"There's a lot of interest in thinking about the value of data," says Professor James Zou, member of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and faculty lead of a new HAI executive education program on the subject. How should companies set prices for data they buy and sell? How much does any given dataset contribute to a company's bottom line? Should each of us receive a data dividend when companies use our data?

Motivated by these questions, James and graduate student Amirata Ghorbani have developed a new and principled approach to calculating the value of data that is used to train AI models. Their approach, detailed in a paper presented at the International Conference on Machine Learning and summarized for a slightly less technical audience in arXiv, is based on a Nobel Prize-winning economics method and improves upon existing methods for determining the worth of individual datapoints or datasets. In addition, it can help AI systems designers identify low value data that should be excluded from AI training sets as well as high value data worth acquiring. It can even be used to reduce bias in AI systems.

[...]

The data Shapley value can even be used to reduce the existing biases in datasets. For example, many facial recognition systems are trained on datasets that have more images of white males than minorities or women. When these systems are deployed in the real world, their performance suffers because they see more diverse populations. To address this problem, James and Amirata ran an experiment: After a facial recognition system had been deployed in a real setting, they calculated how much each image in the training set contributed to the model's performance in the wild. They found that the images of minorities and women had the highest Shapley values and the images of white males had the lowest Shapley values. They then used this information to fix the problem – weighting the training process in favor of the more valuable images. "By giving those images higher value and giving them more weight in the training process, the data Shapley value will actually make the algorithm work better in deployment – especially for minority populations," James says.

 

Excerpted from: HAI "Quantifying the Value of Data"

image of Chuan-Zheng Lee, EE PhD candidate
February 2021

Congratulations to Chuan-Zheng Lee (PhD candidate) and Leighton Pate Barnes (PhD candidate) on receiving the IEEE GLOBECOM 2020 Selected Areas of Communications Symposium Best Paper Award. Their paper is titled "Over-the-Air Statistical Estimation." Professor Ayfer Özgür is their advisor and co-author.

The award was presented by the IEEE GLOBECOM 2020 Awards Committee and IEEE GLOBECOM 2020 Organizing Committee.

 

Please join us in congratulating Ayfer, Chuan-Zheng, and Leighton on receiving this prestigious best paper award!

IEEE Global Communications Conference (GLOBECOM) Best Paper Award Winners

image of IEEE award

image of prof Amin Arbabian
December 2020

Professor Amin Arbabian, Aidan Fitzpatrick (PhD candidate), and Ajay Singhvi (PhD candidate) have developed an airborne method for imaging underwater objects by combining light and sound to break through the seemingly impassable barrier at the interface of air and water.

The researchers envision their hybrid optical-acoustic system one day being used to conduct drone-based biological marine surveys from the air, carry out large-scale aerial searches of sunken ships and planes, and map the ocean depths with a similar speed and level of detail as Earth's landscapes. Their "Photoacoustic Airborne Sonar System" is detailed in a recent study published in the journal IEEE Access.

"Airborne and spaceborne radar and laser-based, or LIDAR, systems have been able to map Earth's landscapes for decades. Radar signals are even able to penetrate cloud coverage and canopy coverage. However, seawater is much too absorptive for imaging into the water," reports Amin. "Our goal is to develop a more robust system which can image even through murky water."

 

Excerpted from "Stanford engineers combine light and sound to see underwater", Stanford News, November 30, 2020

 

Related

image of prof James Zou
November 2020

Professor James Zou, says that as algorithms compete for clicks and the associated user data, they become more specialized for subpopulations that gravitate to their sites. This can have serious implications for both companies and consumers.

This is described in a paper "Competing AI: How does competition feedback affect machine learning?", written by Antonio Ginart (EE PhD candidate), Eva Zhang, and professor James Zou.

James' team recognized that there's a feedback dynamic at play if companies' machine learning algorithms are competing for users or customers and at the same time using customer data to train their model. "By winning customers, they're getting a new set of data from those customers, and then by updating their models on this new set of data, they're actually then changing the model and biasing it toward the new customers they've won over," says Antonio Ginart.

In terms of next steps, the team is looking at the effect that buying datasets (rather than collecting data only from customers) might have on algorithmic competition. James is also interested in identifying some prescriptive solutions that his team can recommend to policymakers or individual companies. "What do we do to reduce these kinds of biases now that we have identified the problem?" he says.

"This is still very new and quite cutting-edge work," James says. "I hope this paper sparks researchers to study competition between AI algorithms, as well as the social impact of that competition."


 

Excerpted from "When Algorithms Compete, Who Wins?"

Stanford HAI's mission is to advance AI research, education, policy and practice to improve the human condition.

Pages

Subscribe to RSS - student