Browsing by Subject "Porous Silicon"

Now showing 1 - 3 of 3
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    A Theoretical Study on Porous-Silicon Based Synapse Design for Neural Hardware
    (IEEE, 2021-12) Sikder, Orthi; Schubert, Peter; Electrical and Computer Engineering, School of Engineering and Technology
    Porous silicon (po-Si) is a form of silicon (Si) with nanopores of tunable sizes and shapes distributed over the bulk structure. Although crystalline Si (c-Si) is already established as one of the most advantageous and promising elements for its technological significance, the additional key aspect of po-Si is its large surface area with respect to its small volume which is beneficial for surface chemistry. In this work, we explore the design of a po-Si based synaptic device and investigate its potential for neuromorphic hardware. First, we analyze several electrical properties of po-Si through density functional theory (Ab Initio/ first principle) calculation. We show that the presence of intra-pore dangling states appears within the bandgap region of po-Si. While the bandgap of the po-Si is well known to be higher than c-Si yielding low carrier density and higher resistance, the appearance of these dangling states can significantly participate in electronic transport through hopping mechanism. Then, we analyze the electric-field driven modulation in the dangling bond through controlled intra-pore Si-H bond dissociation. Such modulation of the dangling state density further allows the tenability of the po-Si conductance. Finally, we theoretically evaluate the current-voltage characteristics of our proposed po-Si based synaptic devices and determine the possible range of obtainable conductivity for different porosity. Our analysis signifies that the integration of such devices in the synaptic fabric can enable significantly denser and energy-efficient neuromorphic hardware.
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    Density Functional Theory (DFT) study of hydrogen storage in porous silicon
    (2018) Boaks, Mawla; Schubert, Peter
    Based on plane wave DFT calculation, we carried out micro level investigation of hydrogen storage in nanoporous silicon (npSi). One quarter of a hexagonal pore with Palladium catalyst placed at the surface has been studied for hydrogen dissociation, spillover, bond hopping, and diffusion for both single catalyst atom and small catalyst cluster consisting of multiple catalyst atoms. All the DFT computations were done in one of the biggest research supercomputer facilities of the world, Big Red II. We opted ABINIT, an open source DFT tool for our computations. Our calculation revealed low dissociation, spillover, and bond hoping energy barrier. The energy required to be provided from external sources to fully recharge the storage medium from a gaseous source at a completely empty state has also been evaluated. Hydrogen diffusion along the inner surface of the pore as a means of bond hopping and the possibility of quantum tunneling, a low temperature phenomena used to spontaneously go over an otherwise less likely high energy barrier have been studied as well. Using these micro level parameter values evaluated from the DFT study, the performance of any potential hydrogen storage material can be compared to a set of characteristics sought in an efficient storage media. Thus, the micro scale feasibility of this novel npSi material based hydrogen storage technology was studied as a part of a STTR Phase I project.
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    Silicon Based Nano-electronic Synaptic Device for Neuromorphic Hardware
    (2024-08) Sikder, Orthi; Schubert, Peter; King, Brian; Rizkalla, Maher; Agarwal, Mangilal
    Porous silicon (po-Si) is a unique form of silicon (Si) that features tunable nanopores distributed throughout its bulk structure. While crystalline Si (c-Si) already boasts technological advantages, po-Si offers an additional key aspect with its large surface area relative to its small volume, making it highly conducive to surface chemistry. In this research, our focus centers on the design of a synaptic device based on po-Si, exploring its potential for neuromorphic hardware applications. To begin, we delve into the analysis of several electrical properties of po-Si using density functional theory (ab initio/first principles) calculations. Notably, we discover the presence of intra-pore dangling states within the bandgap region of po-Si. Although po-Si is known for its higher bandgap compared to c-Si, resulting in low carrier density and increased resistance, the existence of these dangling states significantly impacts its electronic transport. Additionally, we investigate the electric field driven modulation of dangling bonds through controlled intra-pore Si-H bond dissociation. This modulation enables precise control over the density of dangling states, facilitating the tunability of po-Si conductance. Theoretically evaluating the current-voltage characteristics of our proposed po-Si based synaptic devices, we determine the potential range of obtainable conductivity. Finally, we evaluate the performance by integrating porous silicon nanoelectronics devices into neural networks. These devices exhibit superior synaptic plasticity, faster response times, and reduced power consumption compared to other synapses. The research indicates that porous-silicon devices are highly effective in neuromorphic systems, paving the way for more efficient and scalable neural networks. These advancements have significant practical and cost-effective implications for a wide range of applications, including pattern recognition, machine learning, and artificial intelligence. Overall, our analyses reveal that the integration of po-Si based synaptic devices into the neural fabric offers a path towards achieving significantly denser and more energy-efficient neuromorphic hardware. With its tunable properties, large surface area, and potential for controlled conductance, po-Si emerges as a promising candidate for the development of advanced silicon-based nano-electronic devices tailored for neuromorphic computing. As we delve deeper into the potentials of po-Si, the era of cognitive computing, inspired by the elegance of bio-mimetic neural networks, edges closer to becoming a reality.