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Articles 1 - 5 of 5
Full-Text Articles in Nanoscience and Nanotechnology
Thermoelectric Transport In Disordered Organic And Inorganic Semiconductors, Meenakshi Upadhyaya
Thermoelectric Transport In Disordered Organic And Inorganic Semiconductors, Meenakshi Upadhyaya
Doctoral Dissertations
The need for alternative energy sources has led to extensive research on optimizing the conversion efficiency of thermoelectric (TE) materials. TE efficiency is governed by figure-of-merit (ZT) and it has been an enormously challenging task to increase ZT > 1 despite decades of research due to the interdependence of material properties. Most doped inorganic semiconductors have a high electrical conductivity and moderate Seebeck coefficient, but ZT is still limited by their high lattice thermal conductivity. One approach to address this problem is to decrease thermal conductivity by means of alloying and nanostructuring, another is to consider materials with an inherently low …
Function And Dissipation In Finite State Automata - From Computing To Intelligence And Back, Natesh Ganesh
Function And Dissipation In Finite State Automata - From Computing To Intelligence And Back, Natesh Ganesh
Doctoral Dissertations
Society has benefited from the technological revolution and the tremendous growth in computing powered by Moore's law. However, we are fast approaching the ultimate physical limits in terms of both device sizes and the associated energy dissipation. It is important to characterize these limits in a physically grounded and implementation-agnostic manner, in order to capture the fundamental energy dissipation costs associated with performing computing operations with classical information in nano-scale quantum systems. It is also necessary to identify and understand the effect of quantum in-distinguishability, noise, and device variability on these dissipation limits. Identifying these parameters is crucial to designing …
Parallel Algorithms For Time Dependent Density Functional Theory In Real-Space And Real-Time, James Kestyn
Parallel Algorithms For Time Dependent Density Functional Theory In Real-Space And Real-Time, James Kestyn
Doctoral Dissertations
Density functional theory (DFT) and time dependent density functional theory (TDDFT) have had great success solving for ground state and excited states properties of molecules, solids and nanostructures. However, these problems are particularly hard to scale. Both the size of the discrete system and the number of needed eigenstates increase with the number of electrons. A complete parallel framework for DFT and TDDFT calculations applied to molecules and nanostructures is presented in this dissertation. This includes the development of custom numerical algorithms for eigenvalue problems and linear systems. New functionality in the FEAST eigenvalue solver presents an additional level of …
Materials Engineering, Switching Mechanism And Novel Applications Of Memristive Devices, Hao Jiang
Materials Engineering, Switching Mechanism And Novel Applications Of Memristive Devices, Hao Jiang
Doctoral Dissertations
Memristive devices have attracted tremendous interests because of their highly desirable properties such as a simple structure, low switching voltage, fast switching speed, excellent scalability, multiple conductance states and great compatibility with the Complementary Metal–Oxide–Semiconductor technology. Hence, they stand out as promising candidates for next-generation non-volatile memory and electronic synapses in artificial neural network. This thesis reports systematic studies of the memristive switching phenomena in oxide based material systems, in aspects of materials engineering, switching mechanism and novel applications. We demonstrated efficient ways of engineering device performances such as metal doping and further presented a highly reliable hafnium oxide based …
Physically Equivalent Intelligent Systems For Reasoning Under Uncertainty At Nanoscale, Santosh Khasanvis
Physically Equivalent Intelligent Systems For Reasoning Under Uncertainty At Nanoscale, Santosh Khasanvis
Doctoral Dissertations
Machines today lack the inherent ability to reason and make decisions, or operate in the presence of uncertainty. Machine-learning methods such as Bayesian Networks (BNs) are widely acknowledged for their ability to uncover relationships and generate causal models for complex interactions. However, their massive computational requirement, when implemented on conventional computers, hinders their usefulness in many critical problem areas e.g., genetic basis of diseases, macro finance, text classification, environment monitoring, etc. We propose a new non-von Neumann technology framework purposefully architected across all layers for solving these problems efficiently through physical equivalence, enabled by emerging nanotechnology. The architecture builds …