Master of Science
Electrical and Computer Engineering
Henry M. Rowan College of Engineering
Dwaipayan Chakraborty, Ph.D.
Committee Member 1
Wei Xue, Ph.D.
Committee Member 2
Huaxia Wang, Ph.D.
applied artificial intelligence, applied machine learning, bandstructures, feature vectors, non-volatile memory
Computer Engineering | Electrical and Computer Engineering
As industry shifts increasingly towards post-Moore architectures, the importance of identifying non-CMOS devices is of growing importance. However, a large bottleneck towards the discovery of these devices is the high cost-of-entry to their fabrication and testing, despite existing literature providing a large number of successful non-volatile devices and the materials used in their construction. This thesis leverages this literature in tandem with the availability of single-value chemical and physical properties, as well as many-value bandstructures of these materials through the Materials Project database. A method of classifying these materials for classical ML classification is presented, using simple feature vectors of these single value chemical and physical properties, as well as multiple methods of classification which incorporate the many-value calculated bandstructures for these materials. After performing this classification, various explainable AI methods are implemented to further identify material characteristics that contribute to the non-volatile classification of a candidate material.
Pruden, John Raymon, "INTELLIGENT MATERIALS DISCOVERY FOR NON-VOLATILE SWITCHING DEVICES" (2023). Theses and Dissertations. 3158.
Available for download on Friday, September 26, 2025