Sarah Swisher
University of Minnesota, MN·Engineering·Early-career
Electronics face two different pressures. Artificial intelligence (AI) is consuming rapidly growing amounts of electricity in data centers.
- Fields
Engineering
Materials Science
Computer Science
- Topics
in-memory computing
memcapacitor
ferroelectric devices
- Grants
- 2 known grants, through May 2028
What they work on
- Create a vertical ferroelectric memcapacitor, a capacitor that stores memory states, that can be fabricated in the back end of the CMOS process for monolithic 3D chip integration.
- Design memcapacitor-based in-memory computing circuits that perform multiply-and-accumulate operations inside memory cells, and build a deep neural network accelerator with an analog data path and digital control.
- Predict the 3D temperature profile in flexible circuits during photonic curing, determining how geometry, device layout, proximity effects and material properties change curing temperature.
- Determine how photonic curing affects electron traps and transport in oxide semiconductor transistors on plastic, combining electrical and materials characterization with a trap-density model.
- Identify failure mechanisms of plastic substrates and test flexible oxide TFTs under multiple simultaneous stresses that mimic real operating conditions to improve durability.
More about the research
Meanwhile, many new uses, such as wearable and implantable medical sensors, need circuits that bend instead of rigid chips. The grants address both, by rethinking semiconductor devices and how they are made: one project builds memory devices that can also compute, to cut AI's energy use, and the other uses brief flashes of light to make high-quality transistors on plastic.
Why it matters
Cutting the energy used by AI hardware by orders of magnitude could ease the strain on electricity grids and reduce AI's climate impact. Low-cost, high-performance flexible transistors could enable bendable displays, wearable and implantable biomedical sensors, and lightweight durable electronics. The PI's stated long-term goal includes tools for emergency triage, wound monitoring and pediatric devices.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Julia Hsu
University of Texas at Dallas, TX·Materials Science
Design indium-based extreme ultraviolet (EUV) resists, light-sensitive films for patterning chip features, using computational and machine learning tools that optimize both material and device properties.
Raisul Islam
Purdue University, IN·Engineering
Fabricate and characterize the Diffusive Ferroelectric Field-Effect Transistor (DFeFET), combining engineered drain contact metals with amorphous oxide semiconductor channels in a ferroelectric-gated transistor to obtain controllable volatile hysteresis in drain current-voltage curves.
Qian Yu
University of California-Santa Barbara, CA·Engineering
Develop a highly compressed training method for physics-informed neural networks (PINNs) that uses only forward passes and no backpropagation, so it can run on photonic chips without photonic memory.
The school
University of Minnesota: 88 labs on PI Pages, $1.4B a year on research, 722 research doctorates a year.
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