Yield analysis is a critical step in memory designs considering a variety of performance constraints. Traditional circuit level Monte-Carlo simulations for yield estimation of Static Random Access Memory (SRAM) cell is quite time consuming due to their characteristic of low failure rate, while statistical method of yield sensitivity analysis is meaningful for its high efficiency. This thesis proposes a novel statistical model to conduct yield sensitivity prediction on SRAM cells at the simulation level, which excels regular circuit simulations in a significant runtime speedup. Based on the theory of Kriging method that is widely used in geostatistics, we develop a series of statistical model building and updating strategies to obtain satisfactory accuracy and efficiency in SRAM yield sensitivity analysis. Generally, this model applies to the yield and sensitivity evaluation with varying design parameters, under the constraints of most SRAM performance metric. Moreover, it is potentially suitable for any designated distribution of the process variation regardless of the sampling method.

Reliable and scalable manufacturing of nanofabrics entails significant challenges. Scalable nano-manufacturing approaches that employ the use of lithographic masks in conjunction with nanofabrication based on self-assembly have been proposed. A bottom-up fabrication of nanoelectronic circuits is expected to be subject to various defects and identifying the types of defects that may occur during each step of a manufacturing pathway is essential in any attempt to achieve reliable manufacturing. This thesis aims at analyzing the sources of defects in a nano-manufacturing flow and estimating the resulting yield loss. It integrates physical fabric considerations, manufacturing sequences and the resulting defect scenarios. This is in contrast to most current approaches that use conventional defect models and assume constant defect rates without analyzing the manufacturing pathway to determine the sources of defects and their probabilities. The manufacturing pathway will be analyzed for identifying the defects introduced during each manufacturing step in the sequence, followed by yield loss estimation.

The conversion of red oak small diameter timber (SDT) into solid wood products was investigated. The objectives of this research were to 1) determine the yield of lumber, pallet and container parts, and residues from SDT and the market potential for these products; 2) determine the economic feasibility of a SDT sawmill and pallet part mill located in Southwest Virginia; 3) develop a business plan for a SDT sawmill and pallet part mill located in Southwest Virginia. The methods for this research consisted of resource, yield and economic analyses, and the development of a business model. The resource analysis indicated an ample supply of red oak SDT available in Southwest Virginia. The yield analysis used red oak SDT logs, which were manufactured into lumber, container parts and wood residues. The yield of 3" wide container parts from cants varied from 63% to 66%. The 1" nominal lumber produced was mainly 2A and 3A, 74%, and 24% was 1 common. The economic analysis utilized break even, net present value and internal rate of return analyses to determine the economic feasibility of utilizing red oak SDT. The results of the study indicated that the sawmill-only processing level scenario is not economically feasible given the specified conditions and assumptions. However, the results showed that the sawmill and pallet part mill, actual yield scenario at $35/ton delivered log cost is economically feasible. The hypothetical business model for Southwest Custom Hardwoods was economically feasible. The final net present value was calculated to be over $750,000 and the final internal rate of return was 11%. Future yield studies should weigh logs so that the yield of residues and solid wood products can be directly compared. Future research into the utilization of hardwood SDT should include yield studies of other species and other product mixes. / Master of Science

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Application Specific Integrated Circuits (ASICs) are critical to delivering on 5G’s promises of high speed, low latency, and expanded capacity. Digital Front-End (DFE) ASICs are particularly important components because they enhance crucial signal processing activities. It handles duties including carrier mixing, up-sampling, and modulation-demodulation, allowing for efficient data transmission and reception inthe complicated 5G environment. The main aim of this work is to develop a power estimation tool for DFE radio ASICs and to understand the different use cases. It also studies the spread of power consumption, taking into account process and metal variations. The thesis provides a detailed case study of the DFE ASIC, including its Intellectual Property (IP) blocks, configurations, and protocols. It investigates the power consumption of DFE ASICs under various conditions, including active processing, power-saving mode, and no clock. In this thesis we build a power model that describes how the factors affect the ASIC’s power consumption. It also performs spread analysis to evaluate the impact of all factors using MATLAB tool. Based on this we then generate three distributionmodels to study the combined likelihood of the variations. It also uses Monte Carlo simulation to understand total power usage. Through this work we can conclude that the power consumption of DFE ASICs is affected by a variety of factors. The power model and spread analysis can be usedto forecast and optimize power usage in 5G systems.

Global economic trends suggest that there is a need to generate sustainable renewable energy to meet growing global energy demands. Solar energy harnessed by concentrated photovoltaic (CPV) systems has a potential for strong contributions to future energy supplies. However, as a relatively new technology, there is still a need for considerable research into the relationship between the technology and the solar resource. Research into CPV systems was carried out at the University of Ottawa’s Solar Cells and Nanostructured Device Laboratory (SUNLAB), focusing on the acquisition and assessment of meteorological and local solar resource datasets as inputs to more complex system (cell) models for energy yield assessment. An algorithm aimed at estimating the spectral profile of direct normal irradiance (DNI) was created. The algorithm was designed to use easily sourced low resolution meteorological datasets, temporal band pass filter measurement and an atmospheric radiative transfer model to determine a location specific solar spectrum. Its core design involved the use of an optical depth parameterization algorithm based on a published objective regression algorithm. Initial results showed a spectral agreement that corresponds to 0.56% photo-current difference in a modeled CPV cell when compared to measured spectrum. The common procedures and datasets used for long term CPV energy yield assessment was investigated. The aim was to quantitatively de-convolute various factors, especially meteorological factors responsible for error bias in CPV energy yield evaluation. Over the time period from June 2011 to August 2012, the analysis found that neglecting spectral variations resulted in a ~2% overestimation of energy yields. It was shown that clouds have the dominant impact on CPV energy yields, at the 60% level.

Global economic trends suggest that there is a need to generate sustainable renewable energy to meet growing global energy demands. Solar energy harnessed by concentrated photovoltaic (CPV) systems has a potential for strong contributions to future energy supplies. However, as a relatively new technology, there is still a need for considerable research into the relationship between the technology and the solar resource. Research into CPV systems was carried out at the University of Ottawa’s Solar Cells and Nanostructured Device Laboratory (SUNLAB), focusing on the acquisition and assessment of meteorological and local solar resource datasets as inputs to more complex system (cell) models for energy yield assessment. An algorithm aimed at estimating the spectral profile of direct normal irradiance (DNI) was created. The algorithm was designed to use easily sourced low resolution meteorological datasets, temporal band pass filter measurement and an atmospheric radiative transfer model to determine a location specific solar spectrum. Its core design involved the use of an optical depth parameterization algorithm based on a published objective regression algorithm. Initial results showed a spectral agreement that corresponds to 0.56% photo-current difference in a modeled CPV cell when compared to measured spectrum. The common procedures and datasets used for long term CPV energy yield assessment was investigated. The aim was to quantitatively de-convolute various factors, especially meteorological factors responsible for error bias in CPV energy yield evaluation. Over the time period from June 2011 to August 2012, the analysis found that neglecting spectral variations resulted in a ~2% overestimation of energy yields. It was shown that clouds have the dominant impact on CPV energy yields, at the 60% level.