Wed, 11/07/2018 - 4:10pm to 5:00pm
DeMeritt 240
Material Science Seminar
Dr. Jean Anne C. Incorvia, University of Texas at Austin, Electrical & Computer Engineering Department

Neuromorphic Computing with Magnetic Materials


Bioinspired, or neuromorphic, computing is a computing paradigm that takes inspiration from the way the brain operates. Biological information processing features a co-localized memory and logic system where memory is distributed with the processing, and is extremely efficient at tasks that require massive amounts of data to be processed (e.g., recognition, classification, and prediction). For example, when performing tasks such as face or voice recognition, the brain can complete these tasks by consuming a million times less power than modern supercomputers.

Research on neuromorphic computing is strongly driven by the need to invent a new computing topology that can lead to great improvements in computing efficiency. Existing implementations of neural networks have mostly been constructed in software that runs on supercomputers of conventional architecture, losing the promising characteristics of bioinspired algorithms. A significant challenge exists in creating high-density neural networks with all necessary biological functions assembled.

Magnetic material-based devices are particularly suitable to act as neuromorphic computing elements, because they have high endurance for data-intensive tasks and have complex behaviors that can be mapped onto the neuromorphic system. I will present our results modeling that three-terminal magnetic tunnel junctions with mobile domain walls can perform both synapse and neuron functions, including controlled synaptic weights and neurons that integrate, fire, leak, and have lateral inhibition. I will show our progress on fabricating the synapse and neuron devices.



Jean Anne Incorvia is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin. Dr. Incorvia works on developing practical nano devices for the future of computing using emerging physics and materials. This has included research in spintronics, the intersection of 2D materials and spintronics, and using low-dimensional materials for interconnects and transistors.

Dr. Incorvia received her Ph.D. in physics from Harvard University in 2015, cross-registered at MIT, where she was a Department of Energy Graduate Student Fellow. From 2015-2017, she completed a postdoc at Stanford University in the department of electrical engineering, working in nanoelectronics. She received her bachelor’s in physics from UC Berkeley in 2008.