Artificial Intelligence has had a massive burst of growth in the past two years, and will likely impact every job on the market. In my thesis I investigate how AI will affect the creative arts, specifically visual arts, creative writing…
Artificial Intelligence has had a massive burst of growth in the past two years, and will likely impact every job on the market. In my thesis I investigate how AI will affect the creative arts, specifically visual arts, creative writing and film, and why the industry might turn to using AI over human counterparts.
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Modern Complementary-Metal-Oxide-Semiconductor (CMOS) technologies are facing critical challenges: scaling channel lengths below ~10 nm is hindered by significant transport degradation as bulk semiconductors (i.e., silicon) are thinned down, energy consumption is affected by short-channel effects and off-state leakage, and conventional…
Modern Complementary-Metal-Oxide-Semiconductor (CMOS) technologies are facing critical challenges: scaling channel lengths below ~10 nm is hindered by significant transport degradation as bulk semiconductors (i.e., silicon) are thinned down, energy consumption is affected by short-channel effects and off-state leakage, and conventional von Neumann computing architectures face serious bottlenecks affecting performance and efficiency (energy consumption and throughput). Neuromorhic and/or in-memory computing architectures using resistive random-access memory (RRAM) crossbar arrays are promising candidates to mitigate these bottlenecks and to circumvent CMOS scaling challenges. Recently, emerging two dimensional materials (2DMs) are investigated towards ultra-scaled CMOS devices, as well as towards non-volatile memory and neuromorphic devices with potential improvements in scalability, power consumption, switching speed, and compatibility with CMOS integration.The first part of this dissertation presents contributions towards high-yield 2DMs field- effect-transistors (FETs) fabrication using wafer-scale chemical vapor deposition (CVD) monolayer MoS2. This work provides valuable insight about metal contact processing, including extraction of Schottky barrier heights and Fermi-level pinning effects, for next- generation integrated electronic systems based on CVD-grown 2DMs.
The second part introduces wafer-scale fabrication of memristor arrays with CVD- grown hexagonal boron nitride (h-BN) as the active switching layer. This work establishes the multi-state analog pulse programmability and presents the first experimental demonstration of dot-product computation and implementation of multi-variable stochastic linear regression on h-BN memristor hardware. This work extends beyond previous demonstrations of non-volatile resistive switching (NVRS) behavior in isolated h-BN memristors and paves the way for more sophisticated demonstrations of machine learning applications based on 2DMs.
Finally, combining the benefits of CVD-grown 2DMs and graphene edge contacts, vertical h-BN memristors with ultra-small active areas are introduced through this research. These devices achieve low operating currents (high resistance), large RHRS/RLRS ratio, and enable three-dimensional (3D) integration (vertical stacking) for ultimate RRAM scalability. Moreover, they facilitate studying fundamental NVRS mechanisms of single conductive nano-filaments (CNFs) which was previously unattainable in planar devices. This way, single quantum step in conductance was experimentally observed, consistent with theorized atomically-constrained CNFs behavior associated with potential improvements in stability of NVRS operation. This is supported by measured improvements in retention of quantized conductance compared to other non-2DMs filamentary-based memristors.
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The Deep Neural Network (DNN) is one type of a neuromorphic computing approach that has gained substantial interest today. To achieve continuous improvement in accuracy, the depth, and the size of the deep neural network needs to significantly increase. As…
The Deep Neural Network (DNN) is one type of a neuromorphic computing approach that has gained substantial interest today. To achieve continuous improvement in accuracy, the depth, and the size of the deep neural network needs to significantly increase. As the scale of the neural network increases, it poses a severe challenge to its hardware implementation with conventional Computer Processing Unit (CPU) and Graphic Processing Unit (GPU) from the perspective of power, computation, and memory. To address this challenge, domain specific specialized digital neural network accelerators based on Field Programmable Gate Array (FPGAs) and Application Specific Integrated Circuits (ASICs) have been developed. However, limitations still exist in terms of on-chip memory capacity, and off-chip memory access. As an alternative, Resistive Random Access Memories (RRAMs), have been proposed to store weights on chip with higher density and enabling fast analog computation with low power consumption. Conductive Bridge Random Access Memories (CBRAMs) is a subset of RRAMs, whose conductance states is defined by the existence and modulation of a conductive metal filament. Ag-Chalcogenide based Conductive Bridge RAM (CBRAM) devices have demonstrated multiple resistive states making them potential candidates for use as analog synapses in neuromorphic hardware. In this work the use of Ag-Ge30Se70 device as an analog synaptic device has been explored. Ag-Ge30Se70 CBRAM crossbar array was fabricated. The fabricated crossbar devices were subjected to different pulsing schemes and conductance linearity response was analyzed. An improved linear response of the devices from a non-linearity factor of 6.65 to 1 for potentiation and -2.25 to -0.95 for depression with non-identical pulse application is observed. The effect of improved linearity was quantified by simulating the devices in an artificial neural network. Simulations for area, latency, and power consumption of the CBRAM device in a neural accelerator was conducted. Further, the changes caused by Total Ionizing Dose (TID) in the conductance of the analog response of Ag-Ge30Se70 Conductive Bridge Random Access Memory (CBRAM)-based synapses are studied. The effect of irradiation was further analyzed by simulating the devices in an artificial neural network. Material characterization was performed to understand the change in conductance observed due to TID.
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Recently, the implementation of neuromorphic accelerator hardware has gradually changed from traditional Von Neumann architectures to non-Von Neumann architectures due to the “memory wall” and “power wall”. Near-memory computing (NMC) and In- memory computing (IMC) are two common types of…
Recently, the implementation of neuromorphic accelerator hardware has gradually changed from traditional Von Neumann architectures to non-Von Neumann architectures due to the “memory wall” and “power wall”. Near-memory computing (NMC) and In- memory computing (IMC) are two common types of non-Von Neumann approaches. NMC can help reduce data movements, yet it cannot fully address the challenge of improving computational efficiency as the neural network size grows. IMC has been proposed as a superior alternative. This architecture performs computation inside the memory array using stackable synaptic devices to improve the latency and the energy efficiency of neural network accelerators. Both volatile and non-volatile computational memory devices can achieve IMC. Fully complementary metal-oxide semiconductor (CMOS) in-memory computing cells can be realized by adding additional transistors in standard static random access memory (SRAM) bit-cell. The SRAM-based designs investigated in this dissertation perform bit-wise logical operation to obtain XNOR-and-accumulate computation (XAC) for deep neural networks (DNNs). Hybrid in-memory computing architectures combine CMOS with embedded non-volatile memory (eNVM). Resistive random access memory (RRAM) is one class of eNVM ideally suited for hybrid IMC. In a neural network, RRAM with programmable multi-level resistance/conductance states can naturally emulate weight transitions in the synaptic elements of neural networks. In this dissertation, the operation and effects of ionizing radiation effects on both fully CMOS and hybrid IMCs are investigated. The fully CMOS architectures preform SRAM-based XAC computations. The hybrid architectures use multi-state RRAM synapse with CMOS neurons to perform multiply-and-accumulate computation (MAC). In the SRAM XAC array, an 8×8 XNOR IMC array is modeled with flipped-well enhanced-gate super low threshold voltage (EGSLVT) metal-oxide semiconductor field-effect transistors (MOSFETs) from the GlobalFoundries 22nm fully depleted silicon on insulator (FDSOI) process. The impact of total ionizing dose (TID) on the XAC synaptic array is analyzed by using radiation-aware models to mimic TID-induced voltage shifts in MOSFETs. In multi- state RRAM MAC array, 4-state conductance has been programmed in hafnium-oxide (HfOx) RRAM 1-transistor-1-resistor (1T1R) array. The impact of total ionizing dose on the multi-state behavior of HfOx RRAM is evaluated by irradiating a 64kb 1T1R array with 90nm CMOS peripheral circuitry under Co-60 γ-ray irradiation.
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This work is aimed at detecting and assessing the performance of colorimetricgold nanoparticle (AuNP) based biosensors, designed to inspect 17-beta-estradiol (E2), SARS-Cov-2 (RBD), and Ebola virus secreted glycoprotein (sGP) with samples at different concentration ranges. The biosensors are able to…
This work is aimed at detecting and assessing the performance of colorimetricgold nanoparticle (AuNP) based biosensors, designed to inspect 17-beta-estradiol (E2), SARS-Cov-2 (RBD), and Ebola virus secreted glycoprotein (sGP) with samples at different concentration ranges. The biosensors are able to provide a colorimetric readout, that enables the detection signal to be transmitted via a simple glance, which renders these biosensors cheap and rapid therefore enabling for their implementation into point of care (POC) devices for diagnostic testing in harsh /rural environments, where there is a lack of machinery or trained staff to carry out the diagnosis experiments. Or their implementation into POC devices in medical areas for clinical diagnosis. The intent of this research is to detect the targets of interest such as E2 at a lower limit of detection (LOD), and such as RBD using a novel biosensor design. The verification of the colorimetric results is done via transmission spectra recordings and a compilation of the extinction, where an S-curve relative to the detection concentrations can be seen. This research displays, the fabrication of numerous biosensors and using them in detection experiments to hypothesize the performance of detection using target samples. Additionally, this color change is quantifiable by transmission spectrum recordings to compile the data and calculate the extinction S curve. With the least extinction values pertaining to the highest concentration of detection and the highest extinction values is at the lowest concentration of detection.
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A general review of film growth with various mechanisms is given. Additives and their potential effects on film properties are also discussed. Experimental light-induced aluminum (Al) plating tool design is discussed. Light-induced electroplating of Al as the front electrode on…
A general review of film growth with various mechanisms is given. Additives and their potential effects on film properties are also discussed. Experimental light-induced aluminum (Al) plating tool design is discussed. Light-induced electroplating of Al as the front electrode on the n-type emitter of silicon (Si) solar cells is proposed as a substitute for screen-printed Silver (Ag). The advantages and disadvantages of Al over copper (Cu) as a suitable Ag replacement are examined. Optimization of the power given to a green laser for silicon nitride (SiNx) anitreflection coating patterning is performed. Laser damage and contamination removal conditions on post-patterned cell surfaces are identified. Plating and post-annealing temperature effects on Al morphology and film resistivity are explored. Morphology and resistivity improvement of the Al film are also investigated through several plating additives. The lowest resistivity of 3.1 µΩ-cm is given by nicotinic acid. Laser induced damage to the cell emitter experimentally limits the contact resistivity between light-induced Al and Si to approximately 69 mΩ-cm2. Phosphorus pentachloride (PCl5) is introduced into the plating bath and improved the the contact resistivity between light induced Al and Si to a range of 0.1-1 mΩ-cm2. Secondary ion mass spectroscopy (SIMS) was performed on a film deposited with PCl5 and showed a phosphorus peak, indicating emitter phosphorus concentration may be the reason for the low contact resistivity between light-induced Al and Si. SEM also shows that PCl5 improves Al film density and plating throwing power. Post plating annealing performed at a temperature of 500°C allows Al to spike through the thin n-type emitter causing cell failure. Atmospheric moisture causes poor process reproducibility.
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The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was…
The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the engagement in in-depth interviews with professionals that work in the growing, distribution, and processing of leafy greens. Food safety in the leafy green industry is growing in importance in the wake of costly outbreaks that resulted and recalls and lasting market damage. The Dendritic Identifier provides a unique identification tag that is unclonable, scannable, and compatible with blockchain systems. It is a digital trigger that can be implemented throughout the commercial leafy green supply chain to increase visibility from farm to fork for the consumer and a traceability system for government agencies to trace outbreaks. Efforts like the Food Safety Modernization Act, the Leafy Green Marketing Agreement, and other certifications aim at establishing science-based standards regarding soil testing, water, animal feces, imports, and more. The leafy green supply chains are fragmented in terms of tagging methods and data management services used. There are obstacles in implementing Dendritic Identifiers in that all parties must have systems capable of joining blockchain networks. While there is still a lot to take into consideration for implementation, solutions like the IBM Food Trust pose options for a more fluid transfer of information. Dendritic Identifiers beat out competing tagging technologies in that they work with cellphones, are low cost, and are blockchain compatible. Growers and processors are excited by the opportunity to showcase their extensive food safety measures. The next step in understanding the use environment is to focus on the retail distribution and the retailer specifically.
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The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was…
The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the engagement in in-depth interviews with professionals that work in the growing, distribution, and processing of leafy greens. Food safety in the leafy green industry is growing in importance in the wake of costly outbreaks that resulted and recalls and lasting market damage. The Dendritic Identifier provides a unique identification tag that is unclonable, scannable, and compatible with blockchain systems. It is a digital trigger that can be implemented throughout the commercial leafy green supply chain to increase visibility from farm to fork for the consumer and a traceability system for government agencies to trace outbreaks. Efforts like the Food Safety Modernization Act, the Leafy Green Marketing Agreement, and other certifications aim at establishing science-based standards regarding soil testing, water, animal feces, imports, and more. The leafy green supply chains are fragmented in terms of tagging methods and data management services used. There are obstacles in implementing Dendritic Identifiers in that all parties must have systems capable of joining blockchain networks. While there is still a lot to take into consideration for implementation, solutions like the IBM Food Trust pose options for a more fluid transfer of information. Dendritic Identifiers beat out competing tagging technologies in that they work with cellphones, are low cost, and are blockchain compatible. Growers and processors are excited by the opportunity to showcase their extensive food safety measures. The next step in understanding the use environment is to focus on the retail distribution and the retailer specifically.
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The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was…
The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the engagement in in-depth interviews with professionals that work in the growing, distribution, and processing of leafy greens. Food safety in the leafy green industry is growing in importance in the wake of costly outbreaks that resulted and recalls and lasting market damage. The Dendritic Identifier provides a unique identification tag that is unclonable, scannable, and compatible with blockchain systems. It is a digital trigger that can be implemented throughout the commercial leafy green supply chain to increase visibility from farm to fork for the consumer and a traceability system for government agencies to trace outbreaks. Efforts like the Food Safety Modernization Act, the Leafy Green Marketing Agreement, and other certifications aim at establishing science-based standards regarding soil testing, water, animal feces, imports, and more. The leafy green supply chains are fragmented in terms of tagging methods and data management services used. There are obstacles in implementing Dendritic Identifiers in that all parties must have systems capable of joining blockchain networks. While there is still a lot to take into consideration for implementation, solutions like the IBM Food Trust pose options for a more fluid transfer of information. Dendritic Identifiers beat out competing tagging technologies in that they work with cellphones, are low cost, and are blockchain compatible. Growers and processors are excited by the opportunity to showcase their extensive food safety measures. The next step in understanding the use environment is to focus on the retail distribution and the retailer specifically.
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When exposed to radiation, many electronic components become damaged and operate incorrectly. Making sure these components are resistant to radiation effects is especially important for components used in space flight operations. At low dose rates, a phenomenon known as the…
When exposed to radiation, many electronic components become damaged and operate incorrectly. Making sure these components are resistant to radiation effects is especially important for components used in space flight operations. At low dose rates, a phenomenon known as the enhanced low dose rate sensitivity (ELDRS) effect causes an increase in current within linear bipolar circuits. This increase in current is not desirable for space flight operations. Correctly selecting radiation hardened components or figuring out how to deal with the effects for space operation is important, however, radiation testing each component is very expensive and time consuming. To further the future of space travel, a more efficient way of testing is highly desired by the space industry.
A low-cost and time-efficient solution is the IMPACT tool. The Multiscale Tool for Modeling Radiation Effects in Linear Bipolar Circuits project aims to improve the existing IMPACT tool for radiation simulation. This tool contains a database of commonly used linear bipolar circuits and allows the user to model the radiation effects. Currently the tool is not very easy to use and the circuit database is limited. The team’s goal and overall outcome of the project is to deliver the IMPACT tool with a user-friendly interface and an expanded circuit database. The team is using multiple tools to improve the overall appearance of the IMPACT tool and running simulations to collect any necessary data for the database expansion.
In our thesis, Kerri and Kylie are using LTSpice simulations to expand the database. Cheyenne is using TCAD modeling to create TCAD models of transistors and compare them with her other group member’s simulations.
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