Emily Toomey

Massachusetts Institute of Technology

PhD Student

Emily is currently a 5th year PhD student in the Quantum Nanostructures and Nanofabrication Group at MIT, where her research focuses on the development of nanoscale superconducting electronics. Her work has been published in journals such as Physical Review Applied and the Journal of Physical Chemistry C. She is a National Science Foundation Graduate Research Fellow, and a 2019 AAAS Mass Media Fellow writing science journalism at Smithsonian Magazine. In 2017, she was awarded the Ernst A. Guillemin Award for the Best Master’s Thesis in Electrical Engineering at MIT. Prior to coming to MIT, she earned her B.S. with Honors in Electrical Engineering from Brown University, where she was awarded the 2015 Outstanding Senior Award in Electrical Engineering and was elected as the Student Commencement Speaker at the School of Engineering Graduation. Outside of research, Emily spends most of her time oil painting, recently earning Third Prize in the 2019 Schnitzer Award for Visual Arts at MIT.

Research Abstract:

From social media to the rise of blockchain, society is consuming an unprecedented amount of data and energy without any indication of slowing down. While silicon-based systems have greatly advanced to meet these demands, CMOS has reached a state of maturity that leaves limited space for improvement. To keep up with the data revolution, hardware needs a renaissance of its own, looking beyond silicon for new materials and architectures that can support the needs of tomorrow’s technologies.

Superconducting nanowires are an ideal platform for exploring the next generation of hardware, hosting a variety of physics that can be harnessed for low-power circuits. My work aims to control such physics—specifically the thermal nonlinearities in the superconducting-to-normal phase transition—and exploit them to create new devices, such as a relaxation oscillator that could be used for frequency mixing; a multilevel memory cell; and a design for an artificial neuron for spiking neural networks.