Memcapacitive Devices in Logic and Crossbar Applications

Over the last decade, memristive devices have been widely adopted in computing for various conventional and unconventional applications. While the integration density, memory property, and nonlinear characteristics have many benefits, reducing the energy consumption is limited by the resistive nature of the devices. Memcapacitors would address that limitation while still having all the benefits of memristors. Recent work has shown that with adjusted parameters during the fabrication process, a metal-oxide device can indeed exhibit a memcapacitive behavior. We introduce novel memcapacitive logic gates and memcapacitive crossbar classifiers as a proof of concept that such applications can outperform memristor-based architectures. The results illustrate that, compared to memristive logic gates, our memcapacitive gates consume about 7x less power. The memcapacitive crossbar classifier achieves similar classification performance but reduces the power consumption by a factor of about 1,500x for the MNIST dataset and a factor of about 1,000x for the CIFAR-10 dataset compared to a memristive crossbar. Our simulation results demonstrate that memcapacitive devices have great potential for both Boolean logic and analog low-power applications

A Case Study of Physics Education at Regis University: Taking Physics Beyond The Classrooms

Acknowledgement This work is a celebration of my four-year journey at Regis University and the people who have made it a memorable experience. I would like to thank Tom Howe and Lara Narcisi for their guidance throughout the planning and writing processes of this thesis as well as the valuable knowledge and mentorship they have offered during my time in the Regis University Honors Program. I am grateful for the friendship and mentorship of every single member of the Regis University Physics and Astronomy Department, especially Fred Gray and Evan Tilton. Without them, this thesis wouldn’t be in the form it is in now. My sincere gratitude also goes to current and former faculty members who participated in this study: Evan Tilton, Fred Gray, Emily Haynes, Quyen Hart, and Jennifer Jarrell. I would also like to thank all other participants of this study and people who took their time to read various drafts of the thesis. Your inputs are valuable to this study and are essential for the completion of this thesis. Dat Tran May 01 202