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Modeling Of Two-Dimensional And Biological Materials Towards Diverse Nano-Systems Applications, Jatin Kashyap
Modeling Of Two-Dimensional And Biological Materials Towards Diverse Nano-Systems Applications, Jatin Kashyap
Dissertations
This dissertation studies the demonstration of materials ranging from two-dimensional (2D) materials to small bio-molecules using various atomistic/molecular and sub-atomic particles (electron, hole, excitons) modeling techniques for multi-domain applications. Three categories of materials/systems are investigated as follows: 2D materials, biological materials, and complexes of 2D and biological materials.
The first problem demonstrates wrinkles' ubiquitous presence in two-dimensional materials significantly alters their properties. It is observed that water molecules, sourced from ambient humidity or transfer method, can get diffused in between Graphene and the substrate during the Graphene growth. The water diffusion causes/assists wrinkle formation in Graphene, which influences its properties. …
Local Learning Algorithms For Stochastic Spiking Neural Networks, Bleema Rosenfeld
Local Learning Algorithms For Stochastic Spiking Neural Networks, Bleema Rosenfeld
Dissertations
This dissertation focuses on the development of machine learning algorithms for spiking neural networks, with an emphasis on local three-factor learning rules that are in keeping with the constraints imposed by current neuromorphic hardware. Spiking neural networks (SNNs) are an alternative to artificial neural networks (ANNs) that follow a similar graphical structure but use a processing paradigm more closely modeled after the biological brain in an effort to harness its low power processing capability. SNNs use an event based processing scheme which leads to significant power savings when implemented in dedicated neuromorphic hardware such as Intel’s Loihi chip.
This work …