Electrical and Computer Engineering

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This is the collection for the University of Waterloo's Department of Electrical and Computer Engineering.

Research outputs are organized by type (eg. Master Thesis, Article, Conference Paper).

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Now showing 1 - 20 of 2011

Fabrication of atomic force microscope probes with high aspect ratio silicon tips, silicon/silicon nitride cantilevers and stair-shaped handles
(University of Waterloo, 2024-09-10) Pan, Aixi
The atomic force microscope (AFM) is a versatile tool with promising applications in biomedical detection, optical spectroscopy, and material characterization. However, its widespread utilization faces challenges due to limitations in conventional fabrication methods of commercially available probes. Specifically, the standard tips may not meet the requirements for scanning deep or narrow structures accurately, and the rectangular handle design can block a portion of the reflected laser signal, leading to inconsistent feedback. Overcoming these limitations is crucial for expanding the utility and unlocking the full potential of AFM in diverse scientific and technological applications. In this thesis, we propose innovative strategies to enhance the scanning performance of AFM probes with high aspect ratio (HAR) tips and stair-shaped handles. Chapter 3 explores four distinct silicon (Si) AFM tip fabrication methods, each offering unique advantages and contributions to the field. The first method (Section 3.1) integrates non-switching pseudo-Bosch etching with wet sharpening techniques to achieve an exceptional aspect ratio of 1:135, marking a significant advancement in tip fabrication. The second method (Section 3.2) introduces an innovative approach utilizing tapered silicon oxide (SiO2) masks to fabricate Si nanocones with extraordinary apex measuring just 3.54 nm. The third method (Section 3.3) explores a novel two-step cryogenic etching process to yield a controllable tip profile with a slight taper angle of 2.2°. The fourth method (Section 3.4) combines the Bosch process with the pseudo-Bosch process, incorporating periodic oxygen (O2) shrinking steps. This approach achieves a remarkable tip apex sharpness of 20 nm, pushing the boundaries of nanofabrication capabilities. Chapter 4 details the fabrication of a stair-shaped Si handle to mitigate laser blocking. Two techniques are described: one leveraging the loading effect and RIE-lag to attain stage heights of 71 μm, 151 μm, 168 μm, and 287 μm, while the other employs pseudo-grayscale lithography with a titanium (Ti) mask, yielding final stages of 52 μm, 83 μm, 161 μm, and 211 μm. Both these methods are applicable for practical AFM probe fabrication without laser blocking. Chapter 5 delves into the mass fabrication of all-Si HAR AFM probes, merging tips fabricated by the O2 shrinking method with handles fabricated using the loading effect and RIE-lag. Furthermore, Chapter 6 explores the adoption of silicon nitride (SiNx) as an alternative to Si for cantilevers. Amorphous low-pressure chemical vapor deposition (LPCVD) SiNx offers benefits such as low spring constants and precise deposition control, resulting in thin and low-spring constant cantilevers. This configuration minimizes damage to the sample and tip, making it ideal for delicate samples. By combining a SiNx cantilever with a Si tip, the probe capitalizes on both tip apex and thin cantilever advantages, facilitating accurate AFM imaging with high resolution while preventing false images on fragile samples.
Fabrication of Silicon Out-of-Plane Microneedles for Potential Drug Delivery and Interstitial Fluid Extraction
(University of Waterloo, 2024-09-10) Hu, Wenhan
Microneedles represent a successful application of MEMS technology, forming minimally invasive platforms for transdermal drug delivery, body fluid sampling, and diagnostics. Silicon microneedles, in particular, are favored due to their exceptional mechanical strength and biocompatibility. This thesis focuses on the three different fabrication methods of silicon microneedles using MEMS techniques. Initially, we fabricated silicon out-of-plane cone-shaped hollow microneedles with sharp apexes and off-axis pores. This process involved backside hole etching and frontside pillar etching via the Bosch process, followed by pillar sharpening using a HF-HNO3 mixed solution. The resulting microneedles were 160 μm high. However, to penetrate the epidermis and access abundant body fluids for health monitoring systems, taller microneedles longer than 500 μm are required. Fabricating these higher microneedles proved challenging due to difficulties in achieving uniform sharpening through wet etching. To address this, we developed a novel method for fabricating silicon out-of-plane hollow microneedles with beveled tips. This method included frontside slope etching, backside hole etching, and frontside pillar etching, combining anisotropic wet etching and dry etching (Bosch process). The resulting microneedles were approximately 600 μm tall with beveled sharp tips. We tested various fundamental functions of these microneedles by connecting the chip to a syringe using a 3D-printed applicator, successfully demonstrating liquid extraction, liquid injection, and simulated drug delivery process. To minimize the impact of inevitable lateral etching during frontside pillar etching in the Bosch process, we proposed sacrificial structures surrounding the pillars to shield them from lateral etching. Testing two types of sacrificial structures, we found both structures could effectively reduce lateral etching, enabling the fabrication of 370 μm high ring pillars with vertical sidewalls. Additionally, grayscale lithography combined with subsequent Bosch processing presents an effective and flexible method for fabricating complex 3D structures like bevels. We first acquired contrast curve for the photoresist before grayscale lithography. Then we used this technique integrating frontside slope etching and frontside pillar etching into a single step, resulting in the fabrication of hollow microneedles measuring 325 μm in height.
Verifying Explanations for Neural Networks
(University of Waterloo, 2024-09-05) Le, Nham Van
Deep neural networks (DNNs) have been applied in solving many complex tasks across multiple domains, many of which have direct effects on our daily lives: generative models are replacing traditional search engines for answering questions, cars are being driven by neural networks, doctors and radiologists are using neural nets to diagnose patients more efficiently, financial systems are run by automated trading bots, etc. Coupled with the ever-increasing of DNNs' complexity, the need for explaining their predictions and verifying their safety is clear. Generally speaking, verifying a DNN involves checking if it behaves as expected for unseen inputs in a particular region, and explaining a DNN involves interpreting the network's prediction on a given input. Both approaches have their own pros and cons: the output of any input in a verified region is proven to be correct (with respect to a spec), but such regions are minuscule compared to the whole input space, not just because of the performance of the tools, but because of the inherent limits in e-robustness -- the commonly used verification specifications; and while explanation methods can be applied to explain the output given any input, they are post-hoc and hard to judge: does an explanation make sense because the DNN is working close to how a human being process the same input, or because the explanation visualizes the input itself without taking the model in consideration? Our main insight: we can combine both verification and explanation, resulting in novel verification problems towards a robust explanation for neural networks. However, any verification problem (or specification) can not exist in isolation, but in a symbiosis relationship with the tools solving it. When we propose a new spec, it is expected that existing tools cannot solve it effectively, or may not work at all. Interesting problems push developers to improve the tools, and better tools widen the design space for researchers to come up with even more interesting specs. Thus, in this proposal, we are introducing not just novel specifications, but how to solve them by building better tools. This thesis presents a series of results and research ideas based on that insight. First, we show that by extending e-robustness with an explanation function (the activation pattern of the DNN), we can verify a bigger region of the input space using existing verification tools. Second, by verifying the explanation functions, we provide a robust way to compare different explanation methods. Finally, even when the combination of existing DNNs' verification specs and explanation functions is friendlier to existing verification tools, we still run into scalability issues as we increase the size of the networks. Thus, in this thesis we also present our results on building a distributed SMT solver, which lies at the heart of many neural network verification tools.
Improving Knowledge Distillation by Training Teachers to maximize Their Conditional Mutual Information
(University of Waterloo, 2024-09-03) Ye, Linfeng
Knowledge distillation (KD) and its variants, as effective model compression methods, have attracted tremendous attention from both academia and industry. These methods usually use the pretrained teacher models' outputs and the ground truth labels as supervision signals to train the lightweight student model, which can improve student performance in terms of accuracy. One aspect of KD that has rarely been explored in the literature is how the behavior of the teacher models affects the students' performance. Specifically, in most existing KD projects, teacher models are usually trained to optimize their own accuracy. However, recent studies have shown that a teacher with higher accuracy does not always lead to a student with higher accuracy \cite{cho2019efficacy, stanton2021does}. To explain the aforementioned counter-intuitive observations and advance the understanding of the role of teacher models in KD, the following research problem naturally arises: \textit{How can a teacher model be trained to further improve student's accuracy in scenarios where the teacher is willing to allow its knowledge to be transferred to the student in whatever form?} In this thesis, we assert that the role of the teacher model is to provide contextual information to the student model during the KD process. In order to increase the contextual information captured by the teacher model, this thesis proposes a novel regularization term called Maximum Conditional Mutual Information (MCMI). Specifically, when a teacher model is trained by conventional cross-entropy loss plus MCMI, its log-likelihood and conditional mutual information (CMI) are simultaneously maximized. The new Class Activation Mapping (CAM) algorithm further verified that maximizing the teacher’s CMI value allows it to capture more contextual information in an image cluster. Via conducting a thorough set of experiments, we show that by employing a teacher trained by CE plus MCMI rather than one trained CE in various state-of-the-art KD frameworks, student's classification accuracy consistently increases, with a gain of up to 3.32\%. In addition, we show that such improvements in the student's accuracy are more drastic in zero-shot and few-shot settings. Notably, when 5\% of the training samples are available to the student (few-shot), the student's accuracy increases with the gain of up to 5.72\%, and increases from 0\% to as high as 84\% for an omitted class (zero-shot).
Low Power TFT Logic Implementation on Display Backplane, Using Unipolar TFTs
(University of Waterloo, 2024-08-30) Kapar, Sparsh
Active-Matrix (AM) displays are key components in devices such as computer monitors, smartphones, laptops, portable gaming consoles, and wearable devices. These displays are trending towards becoming the primary display technology in today’s market. The demand for current displays has surpassed 4K (4096 rows by 2160 columns) and have even reached 8K Ultra-High-Definition (UHD, 7680 rows by 4320 columns). The display panel is generally fabricated on low-cost thin-film transistors (TFT), while the driver and control circuits are built using conventional Complementary-Metal-Oxide-Semiconductor (CMOS) circuits. A bonding pad serves as interface between CMOS circuits and TFT display panel. For each row and column of pixels added to the display backplane, an additional bonding pad needs to be added to properly interface the off-panel peripheral row and column control circuit with TFT and OLED pixel array. As the pixel density of the display increases, the bonding pad pitch must decrease, to accommodate. However, the pitch can only reduce finitely, and this imposes a bottleneck on achieving high-density large-scale displays. Additionally, with each row-line connected to a gate of thousands of pixels, there is a high capacitive load, which contributes to a high dynamic power consumption. Recent research has been investigating the use of TFTs to change the off-panel integrated-circuit (IC) and integrate it onto the display backplane, eliminating the need for bonding pads, while also reducing the overall power consumption. Amorphous silicon (a-Si:H) TFTs have good uniformity and low mask count fabrication process, making it suitable for large-scale displays, in comparison to Low-Temperature-Polysilicon (LTPS) TFTs. However, a-Si:H TFTs are naturally unipolar, which make it hard to replicate the CMOS like logic that the off-panel ICs have. This thesis aims at tackling the bottlenecks of large-scale displays, that is, the high dynamic power consumption, and the limited space from the bonding pads. The proposed row driver circuit presented in this thesis can be used to eliminate the off-panel row IC, and be integrated into the display backplane, while reducing the dynamic power consumption, with a-Si:H TFT technology.
Fast and Efficient Calibration for Phased Arrays and Beamforming Circuits using Novel Constellation Characterization Method and Channel Transformation Technique
(University of Waterloo, 2024-08-30) Chen, Yuxuan
Traditional phased array calibration methods, like exhaustive search, are time-consuming. This thesis proposes a novel, efficient technique that significantly reduces calibration time without sacrificing performance. The proposed approach utilizes a constellation characterization method. It extracts and describes element responses using a set of mapping functions based on a strategically chosen data subset. A custom solver then generates control codes for desired beam patterns. For robustness, a closed-loop calibration routine is introduced to verify the solutions. Additionally, a taper awareness scheme is incorporated to optimize the output power by accounting for element variations and the desired tapering profile. The proposed method demonstrates significant speed-up compared to the exhaustive search method. On two beamforming integrated circuits (BFICs) and two test arrays, it achieves improvements of up to 1100 times while maintaining performance. Furthermore, a channel transformation technique is proposed to leverage similarities between array elements. This technique reduces measurements by transferring mapping functions between array elements, avoiding full characterization for each element. A sequencing technique is also introduced to optimize the transformation route, maximizing success rates and further minimizing measurements. Experimental validation shows significant reductions in addition to the savings achieved via the proposed characterization method. Compared to the exhaustive search method, reductions of up to 3000 times are achieved. This thesis presents significant advancements to phased array calibration, paving the way for efficient and scalable solutions in future high-resolution massive multiple input and multiple output (MIMO) systems.
Resource Management for Vehicular See-through Service Provisioning
(University of Waterloo, 2024-08-29) Wang, Ruoxu
Vehicular see-through service is one of the driver assistance services expected to be provided by smart vehicles in the future. It is envisioned that this service will expand the vision of a smart-vehicle driver by timely alerting the driver to obstacles in the front blind spots. In this study, we investigate cooperative perception and resource allocation in the provision of the vehicular see-through service to ensure that drivers are able to obtain accurate and timely environment information about their front blind spots. To cope with the consumption of communication and computing resources associated with the transmission and processing of multi-view point clouds, we propose a cooperative sensor data collection and processing scheme. In this scheme, smart vehicles cooperatively collect point cloud data for each object and complete the point cloud processing at the on-board computing unit. For each object, we select a set of collectors to collect the point cloud data and an analyzer to process the data. To address the tradeoff between classification accuracy and resource efficiency, we investigate the impact of the location of cooperative vehicles on object classification accuracy and propose an indicator to assess the quality of the collector sets. We develop a collector set pre-selection algorithm that identifies all collector sets for each object, to satisfy the classification accuracy and have minimal data redundancy. To address the tradeoff between the service requirements and the overall resource consumption of the system, we treat each potential collector set and its analyzer of an object as a worker pair and select the worker pair for each object based on its resource consumption. Taking into account the resource competition among worker pairs, we develop a joint vehicle selection and resource allocation algorithm based on ant colony optimization to minimize the overall resource consumption, while satisfying the delay requirements of the service. Simulation results demonstrate that our proposed service provisioning scheme outperforms benchmark schemes in terms of resource efficiency, task completion rate and request completion rate.
Nonclassicality of Propagating States From 3-Photon Interactions in a Superconducting Parametric Cavity
(University of Waterloo, 2024-08-28) Jarvis-Frain, Benjamin
The multimode superconducting parametric cavity has proven to be a powerful and versatile system for producing nonclassical states of light in the microwave regime. Utilizing its ability to realize nonlinear and multimode Hamiltonians, we can produce strongly correlated propagating signals from the cavity at different frequencies including entangled photons. In this thesis, we study the generation of photon triplets using a cubic Hamiltonian in the cavity under a parametric drive. We demonstrate the implementation of 3-photon Spontaneous Parametric Down-Conversion (SPDC) into different frequency modes of the cavity and study the non-Gaussian statistics of the outputted photon triplets through purely linear detection. We detail our methodology for performing absolutely calibrated measurements of the cavity output using a Shot Noise Tunnel Junction (SNTJ) as well as our use of a near quantum-limited Travelling-Wave Parametric Amplifier (TWPA). In addition to the primary results of this thesis, we present calibrated measurements of the noise temperature of the Crescendo TWPA from QuantWare. Through the use of this TWPA and SNTJ, we are able to obtain the correlations between frequencies with low uncertainty up to the 4th moments. From these moments, we can compute a nonlinear entanglement witness on the propagating triplets and demonstrate the non-Gaussian genuine tripartite entanglement between photons with over 15 sigmas of certainty.
Modeling and Simulation of Multilayer MoS2 Schottky Barrier Field-Effect Transistors
(University of Waterloo, 2024-08-28) He, Zhuoyang
As a member of the transition metal dichalcogenide (TMDCs) family, Molybdenum Disulfide (MoS2) exhibits a layered 2D structure with exceptional electronic and optoelectronic properties, making MoS2 a promising candidate for next-generation nano-devices. However, despite numerous efforts in synthesis, fabrication process, and device structure for few-layer MoS2. A key challenge that remains is the Fermi-pinning effect. Due to defects distributed at the MoS2/metal interface, the Schottky-mott rule fails to predict the Schottky barrier height based on the Fermi-level difference between MoS2 and the metal. Instead, the Fermi level of the metal is pinned near the conduction band edge regardless of the work function of the metal used. This phenomenon results in an inevitable Schottky barrier, which must be recognized in device simulations. In this thesis, the electrical and optoelectronic performance of multilayer MoS2 field-effect transistors is predicted and analyzed through simulation techniques. Drift-diffusion equations are employed to model electronic properties, utilizing finite element methods (FEM) to solve the corresponding partial differential equations. FEM discretizes space and divides the solution domain into finite elements. For optoelectronic simulations, When solving Maxwell's equation for optical absorption and carrier generation rates. Finite-Difference Time Domain (FDTD) methods are applied, which discretize both space and time, representing fields as discrete values on a grid in both dimensions. We examine the effect of Schottky barrier height on MoS2-based devices and complete the missing p-branch of MoS2 SBFETs due to the Fermi-pinning effect. Initially, we verify our model against experimental data, results prove the capability of our model to predict the electrical performance of Schottky barrier FETs. By varying the Schottky barrier height from 0 eV to 1.3 eV across the MoS2 bandgap, a transition from n-type transport to p-type transport is observed. However, the ambipolar transport is limited by the relatively large bandgap. Ambipolarity is enabled through asymmetric metal configurations, as evidenced by simulations showing that the Pd-Au configuration can achieve ambipolar behavior with currents comparable to symmetric MoS2-based FETs. Furthermore, we simulate dual-gated FETs by incorporating an additional gate, allowing for reconfigurability between NP and PN configurations. These devices exhibit an outstanding rectification ratio that can be optimized under low gate voltage conditions. Nevertheless, the asymmetry in performance between PN and NP configurations indicates the significant impact of metal choices. The successful establishment of p-n junction in the dual-gated devices illustrates their potential as photodetectors. Simulation results indicate a photoresponsivity of 24.2 mA/W for PN configuration.
Predictable Cache Subsystem Design for Real-time Multicores
(University of Waterloo, 2024-08-23) Wang, Xinzhe
This thesis focuses on the design of the cache subsystem for multicore real-time systems. Specifically, it looks at the two common contention sources in the cache subsystem that undermine the system's timing predictability: temporal contention (queuing delays resulting from simultaneous shared resource accesses) and spatial contention (conflicts in cache line allocations). Accordingly, this thesis proposes two solutions to address the two problems. First, to address temporal contention induced by the shared last-level cache (LLC), we propose PECC, a predictable exclusive cache coherence mechanism. Unlike the common choice of inclusive cache hierarchies, PECC incorporates an exclusive LLC free of back invalidation, which is a significant contributor to the per-request worst-case latency (PR-WCL) in inclusive hierarchies. As a result, PECC reduces the bound of PR-WCL by 6% and improves the average performance by 2.33 times over the predictable solution with an inclusive LLC. Second, to address spatial contention in the shared LLC, we propose ParRP, a novel cache partitioning scheme that provides cache space isolation for shared data. Conventionally, achieving cache space isolation for shared data is challenging due to its intrinsic sharing nature, which violates the resource isolation principle. ParRP overcomes this by partitioning the replacement policy instead of cache entries. Consequently, ParRP guarantees isolation property even with shared data, enabling isolated worst-case execution time (WCET) analysis without interference from other cores. Our evaluation shows that this leads to a 2.4 times reduction in the WCET of multi-threaded tasks with shared data at the cost of a 16.5% decrease in average performance.
A Novel PLC Front-haul for 5G IoT Indoor Communication using Split C-RAN Architecture
(University of Waterloo, 2024-08-23) Ibrahim, Mai
The demand for efficient telecommunications in the era of Fifth Generation (5G) and Internet of Things (IoT) necessitates innovative approaches to network architecture and communication technologies. Recently, split Centralized Radio Access Network (C-RAN) architecture, characterized by Central Unit (CU), geographically dispersed Distributed Unit (DU), and indoor Radio Units (RUs), has presented opportunities for optimizing communication links in indoor environments. Yet, the adaptation of this innovative architecture to enable massive indoor IoT applications is still deemed inefficient due to the associated cost of deployment. Accordingly, this research investigates Power-Line Communication (PLC) as a cost-efficient alternative solution for C-RAN front-haul. Specifically, the focus is on exploring the utilization of indoor low-voltage power lines in the context of 5G New Radio (NR) indoor IoT applications. First, to ensure that standard protocols like Common Public Radio Interface (CPRI) and Enhanced Common Public Radio Interface (eCPRI) can run on PLC, we introduce two novel patented components to the architecture, namely the CPRI-PLC-Gateway (CPG) and Enhanced CPRI-PLC-Gateway (eCPG). These are a plug and play components that come in pairs. They are used to create a virtual PLC front-haul link ensuring transparent transportation of unmodified CPRI or eCPRI frames between DU and RUs, even under challenging PLC channel conditions. As such, they set the foundation for optimizing the PLC front-haul and help resolve various challenges, including PLC time-varying nature and susceptibility to additive white Gaussian noise (AWGN). Furthermore, investigations are extended to study the impact of the proposed PLC based split C-RAN system in the context of the Radio access network (RAN). For that, an indoor multi-story service building that houses a large number of air-interfaced IoT devices is considered. To ensure that the reported results apply to real-life applications, we consider a PLC network that encompasses typical indoor low-voltage 3-phase power lines and follows TN-S earthing configuration. Accordingly, it is shown that through the incorporation of the CPG and eCPG components, the implementation of In-band full-duplex (IBFD) communication over the multiple Input - multiple output (MIMO) PLC channel, and the integration of the hybrid circuit-based isolation, the system can support a considerable number of air interfaced IoT devices at standardized rates. It is also shown that the self-interference over the power line segment is mitigated which ensures robust bidirectional communication in the system. Moreover, a significant aspect of the thesis revolves around conducting a comprehensive performance analysis for the proposed PLC front-haul for IoT indoor communications. Mathematical models, rooted in queuing theory, Markov modelling, and stochastic geometry, are developed to assess the end-to-end delay performance of the indoor front-haul solution. Analytical expressions are derived for various performance metrics, including radio coverage probability, the number of served devices, and system delay. Wireless spatial models, path-loss models, and interference considerations are meticulously analysed in terms of multiple factors such as the number of wireless IoT devices, radio and PLC bandwidth, and transmission technology, in regard to the delay performance of the proposed system. These models are rigorously validated through extensive simulations, demonstrating compliance with stringent 5G, CPRI, and eCPRI bit error rate (BER) and delay requirements. Last, as the thesis further aims to examine the optimization challenge of maximizing throughput in a split-RAN system that includes a PLC front-haul link within a multi-story building. The goal is to optimize the number of fulfilled IoT devices while simultaneously satisfying their quality of service (QoS) criteria. The optimization problem is defined as a mixed-integer non-convex problem, which includes several objectives: maximizing the number of satisfied devices, minimizing operating cost, minimizing device transmit power, and minimizing PLC access delay. The thesis further explores the application of an Evolutionary Multi Objective Optimization (EMO) algorithm, specifically Non-dominated Sorting Genetic Algorithm II (NSGA-II), to address the issue of conflicting objectives in communication systems. The method operates by systematically generating successive iterations of solutions using tournament selection, single-point binary and simulated binary crossovers, and polynomial mutation operators. The system outcomes present a Pareto front consisting of non-dominated solutions for the issue defined using multi-objective optimization (MOO) showing a trade-off between the system objectives.
Emerging Electricity Markets: Including New Energy Storage Technologies & Integrating DERs via ISO-DSO Coordination
(University of Waterloo, 2024-08-22) Goyal, Anshul
Most countries have set a vision of net-zero Greenhouse Gas (GHG) emissions by 2050; however, based on current trends, many of them are lagging in meeting the targets, even for 2025. Energy transition, shifting from fossil-fuel based to clean resources, is a critical step toward achieving Net-Zero Emission (NZE) targets, and is being explored worldwide. The ongoing effort to support the transition to a decarbonized system is to deploy large-scale Renewable Energy Sources (RES); but even after the remarkable increase in deployment of RES, it still seems impossible to achieve decarbonization targets. Various countries, including Canada, have pledged to achieve NZE grid by 2035 and system operators have developed their decarbonization pathways with target objectives and timelines to attain this goal. In this context, green hydrogen-based systems emerge as a potential zero-carbon solution to meet the CO2 emission reduction targets. The electricity sector is recognized as vital for energy sector transformation, in order to achieve NZE goals, as there are already low and emission free resources in this sector such as RES, hydro, etc., and it can easily integrate with other sectors (heat, transport, etc.) as part of the electrification drive. The continuously growing demand for electricity is a challenge to energy security, grid resiliency and results in exorbitant energy prices during peak demand periods. The intermittent nature of RES imposes limits on their use and their variability leads to imbalances between the grid demand and supply. To meet these challenges, the power system requires flexible resources, for which, various alternatives have been proposed including Distributed Energy Resources (DERs), Demand Response (DR) and Energy Storage Systems (ESSs), which seem to be the most promising ones. Also, there have been remarkable advancements in the arena of smart grid, which encourages consumers to deploy DERs and re-profile themselves as prosumers. Different regulating bodies and utilities worldwide are re-organizing their electricity markets to be future-ready with high-DER vision, and are developing coordination models between the Independent System Operator (ISO) and Distribution System Operators (DSOs) to integrate DERs and realize their true potential for the whole system (transmission and distribution). This thesis first presents a novel, Green Hydrogen Systems (GHSs) integrated, Uniform Marginal Price (UMP)-based Day-Ahead Market (DAM) framework and the mathematical model for electricity market auction. The wholesale electricity market participation of GHSs, comprising electrolyzers, storage tanks and fuel cells, is examined considering their bids and offers for charging and discharging modes, respectively. To support transition toward achieving an NZE grid, the effects of inclusion of GHS in the DAM with different emission control strategies, such as emission cap and carbon pricing are examined. Two real systems with distinct characteristics, Alberta and Ontario provinces of Canada, are considered. Subsequently, this thesis presents an extension of the GHS integrated UMP-based market model to Hydrogen-based Emission Free Resources (HEFRs) included Locational Marginal Price (LMP)-based DAM model by formulating appropriate mathematical model, complying with the existing market rules. Comprehensive case studies and sensitivity analysis are carried out to examine the impact of integration of HEFRs on market settlement, marginal prices and system emissions during normal, congestion and under RES uncertainties scenarios. Next, this thesis presents a novel framework with new mathematical models that integrate DR and Battery Energy Storage Systems (BESSs) simultaneously in an LMP-based Multi-Settlement Market (MSM), i.e. a coordinated DAM and Real-Time Market (RTM). A new set of generator ramping constraints, developed from the DAM settlement, and referred to as Day-Ahead Load-Following (DALF) Ramp, are included in the RTM auction model. The performance of the mathematical models are tested on the IEEE 24-bus Reliability Test System (RTS) by carrying out various case studies and scenarios, uncertainty and sensitivity analyses. Effect of DR and BESS characteristics such as level of participation, initial State-of-Charge (SOC), discharge rate, etc. on market settlement is examined. The results demonstrate the merits of the proposed framework, and the impact of the DALF Ramp, DR and BESS inclusion in the MSM auction models on marginal prices, market settlement and system operation. Finally, the thesis presents a new Cooperation Algorithm and a parallel-hierarchical framework for coupling the wholesale and retail electricity markets in order to facilitate the participation of DERs including small-capacity Behind-the-meter DERs (BTM-DERs), in a competitive and equitable manner. The detailed mathematical models of ISO-DSOs coordinated, wholesale and retail market settlements for day-ahead period are developed. The models are tested on the IEEE 24-bus RTS (wholesale market) and multiple 33-bus distribution systems (retail markets). Results demonstrate the effectiveness of the proposed framework over a centralized wholesale market in terms of computational time and over the sequential structure, in terms of DERs’ increased participation, reduced market prices, congestion management, emissions reduction and overall system operation.
Multi-Area Architecture for Real-Time Feedback-Based Optimization of Distribution Grids
(University of Waterloo, 2024-08-22) Farhat, Ilyas
The transition to a more environmentally-friendly power system, predominantly driven by Distributed Energy Resources (DERs) such as smart loads, Electric Vehicles (EVs), and Photovoltaics (PVs) systems, signals a shift towards a new structural paradigm. Significant challenges have emerged as more DERs, particularly those interfaced through inverters, are integrated into the grid. These challenges include variability in power supply and reduced rotational inertia, which contribute to more frequent frequency events and grid instabilities globally. Despite these challenges, DERs offer potential solutions by participating in ancillary service provision. This thesis aims to harness the potential of DERs at the distribution network (DN) as their integration increases. We focus on overcoming coordination challenges between distribution and transmission networks to integrate DN-DERs in frequency support. To achieve this goal, we develop a coordination framework for distribution networks to manage the DERs. Subsequently, we integrate this DN framework with a recently proposed fast frequency control scheme at the transmission network (TN) level. In the first stage, we develop a hierarchical feedback-based control architecture for DN-DER coordination. This architecture enables DNs to swiftly respond to power set-point requests from the Transmission System Operator (TSO) while adhering to local operational constraints and ensuring data privacy. The scheme minimizes inter-area communication needs by leveraging physically adjacent areas within the DN control hierarchy. Rigorous stability analysis establishes intuitive closed-loop stability conditions, accompanied by detailed tuning recommendations. Case studies on multiple feeders, including IEEE-123 and IEEE-8500, validate the architecture using a custom MATLAB®-based application integrated with OpenDSS©. Results demonstrate scalability and effective coordination of DERs in response to TSO commands while managing local DN disturbances and operational limits. In the second stage, we integrate the developed DN control framework into a TN fast frequency control scheme by incorporating a simplified linear model of DN dynamics into the TN control design framework. This integrated approach aims to enhance system responsiveness and performance. To validate this approach, we conducted case studies using the IEEE 9-bus TN system, incorporating IEEE-123 DNs structured according to the hierarchical control framework developed in stage 1. The TN controller, designed with the integrated DN dynamic model, demonstrated improved performance across various DN feeder configurations and tuning scenarios. Combining these stages yields a comprehensive solution that enhances overall system stability and performance. By optimally utilizing DN-DERs to respond to the TN controller, which is designed with awareness of DN-DERs dynamics, the integrated solution resulted in improved response times and reduced oscillations.
Physics-based Compact Model of GaN/AlGaN Schottky Barrier Diode and SiC MOSFET
(University of Waterloo, 2024-08-21) Feng, Yijing
Silicon Carbide (SiC) and Gallium Nitride (GaN) are actively employed in Power Electronics (PE) circuits due to their superior material properties. They both have wide bandgap, high carrier saturation velocity and high thermal conductivity, enabling High Voltage (HV), High Frequency (HF) and High Power (HP) applications. Besides using materials with superior properties, device structure innovations further improve power device performance, such as multi-dimensional structures including super-junction, multi-channel and FinFETs. Compared with one-dimensional structure, it could enhance trade-off between Breakdown Voltage (BV) and On-state Resistance (RON). In order to facilitate circuit design using SiC/GaN transistors with innovative device architectures, there is a growing demand of accurate, scalable, robust and standardized compact models. Using an innovative modular approach, this thesis first proposes a compact model for multi-channel AlGaN/GaN Schottky Barrier Diode (SBD) for kilo-volt applications. The modelling approach, detailed model formation as well as model evaluation against an experimental SBD with five stacked 2DEG channels are explained in detail. Using a similar modular formulation method, the thesis also proposes Waterloo Virtual-source SiC Compact Model (WAVSiC), a comprehensive and user-friendly physics-based compact model for SiC MOSFETs. Accuracy, flexibility and scalability are demonstrated for WAVSiC via benchmarking against measurements of commercial devices. The WAVSiC model is also validated for computational efficiency and robustness, circuit simulator compatibility and ready for Process Design Kit (PDK) integration. In summary, by using a modular approach, the thesis introduces two innovative physics-based compact models for wide-bandgap power electronic devices with accuracy, scalability, and computational efficiency, enabling efficient circuit simulation and PDK development based on these device technologies.
Social-Influence-Based Control of Voluntary-Behaviour Adoption in Small Groups using Agent-Based Modelling
(University of Waterloo, 2024-08-21) Fernandes, Keegan
In this thesis, we take a systems control approach to explore various control influence techniques and investigate how effective they are at changing behaviour. We develop a psychology-based discrete-time behavioural change model of individuals in a group environment. The developed model incorporates work on individual behavioural change, namely the reasoned action approach, to represent how individual beliefs affect behaviour. Additionally, the model uses ideas from the study of social influence to account for social effects on individual beliefs such as conformity and internal biases. Feedback mechanisms are used to integrate the two models. Simulations of the developed model display characteristics of clustering and polarisation, consistent with empirical evidence. The impact of internal bias in driving this phenomenon is noted and developed into an external influence strategy. This strategy along with several others is evaluated using simulations of the model. The results of these simulations show that taking advantage of internal biases is an effective influence strategy. Additionally, results show that an influence strategy of spending time with a different individual in a group at each interaction, rotating to different individuals, can lead to more individuals performing a behaviour when compared to a ``one to all" influence technique where an influencer tries to convince the whole group at once. The developed control strategies are deployed on a real-world test scenario and the behavioural change results are compared to those of simulation. Results show that the developed model produces behavioural outcomes whose trends are consistent with real-world results. These results provide evidence that the model can be used to predict the effects of different influence techniques and to investigate which influence strategies are effective at changing behaviour and which are not. The results also provide insight into how behaviours propagate through a group environment.
Identity and Security in 5G Authentication
(University of Waterloo, 2024-08-21) Parkin, Julian
In this thesis, we study the provision and protection of user identity in the 5G Authentication and Key Agreement (5G-AKA) protocol. We present two variations of the protocol: the first mitigates a family of de-anonymization attacks that aim to defeat the privacy-protection features of 5G-AKA. It does so by replacing a fixed user identity with a sequence of ephemeral identifiers. This variant is designed to be fully backwards compatible with the existing 5G-AKA authentication message formats, which allows it to be used in roaming scenarios without changes to the visited network. The second protocol is a realization of "Bring Your Own Identity" (BYOI) for 5G-AKA, allowing subscribers to authenticate with an identity provisioned by an external provider. This is accomplished by composing 5G-AKA with OAuth 2.0, a de-facto standard for third-party authorization online. We built and verified a formal model of each protocol using Tamarin, a theorem-prover tool for security protocols. From this, we note some limitations of existing formalizations of secrecy and authentication properties, and propose improvements. Finally, we present an implementation of our BYOI protocol over a simulated 5G system, and show it works against Google's OAuth 2.0 API. We discuss some practical considerations arising from the implementation.
Quantum Algorithms for Clustering and Covert Factoring
(University of Waterloo, 2024-08-20) Gopalakrishnan, Dhruv
This thesis is composed of two projects – a quantum algorithm for clustering based on CERN’s event reconstruction algorithm, and a scheme to hide Shor’s algorithm in Hamiltonian Simulation and Ground State Estimation circuits. Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges stemming from datasets spanning several spatial dimensions. In fact, the best-performing clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qLUE is inspired by CLUE, CERN's algorithm developed to address the challenging time and memory budgets of Event Reconstruction (ER) in future High-Energy Physics experiments. As such, qLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qLUE in several scenarios, demonstrating its effectiveness and proving it to be a promising route to handle complex data analysis tasks – especially in high-dimensional datasets with high densities of points. The code we developed for these simulations is available at: https://github.com/godspeed5/QLUE. The advent of large-scale quantum computers promises transformative advances across various fields including optimization, materials science, and cryptography. However, this also poses a threat to traditional cryptography, due to Shor’s algorithm, which efficiently factors large integers. The existence of this algorithm undermines widely-used cryptographic protocols based on integer factorization and discrete logarithms. Even with Post-Quantum Cryptography, attacks of the “save now, decrypt later” type can compromise the security of critical systems. Keeping this mind, we would like to develop quantum systems that are designed specifically for benign applications such as Hamiltonian Simulation or Ground State Estimation – which could be of importance to the pharmaceutical industry. However, it cannot be taken for granted that even such a system is secure from malicious users attempting to run Shor’s Algorithm. In this note we propose the idea of using known circuit-to-Hamiltonian mappings to hide Shor’s algorithm in Hamiltonian simulation and Ground state estimation circuits. We provide the resource estimates for these mappings, and also propose some methods to potentially reduce these overhead costs.
Design and Density Control of a Swarm of Bimodal Particles
(University of Waterloo, 2024-08-16) Shaw, Justine
In this thesis, we present the design and control of a swarm of bimodal particles that switch their geometric shape between two modes. The particles are designed and 3D printed using layers of thermoplastic polyurethane(TPU) and polylactic acid(PLA) materials and are shaped like dodecahedrons. The switching control of the geometric shape is due to the particles reaction with an external stimuli of temperature, inducing switching between a open mode classified as mode 1 and an closed mode classified as mode 2. To enact the changes in geometric shape various temperature based hot and cold programming methods were conducted using water and artificial and natural heat sources. To quantify the aggregation of the swarm and control the switching motion we utilize the metric of the Motility-Induced Phase Separation (MIPS) index. To control the particles using the MIPS index we identify the motion model of the swarm as a Brownian particle or a noisy unicycle, having different parameters for both modes. In this research we experimentally validate the noise parameters that directly affect the motion of the particles thus affecting the MIPS index, and allowing us to control the swarm more directly. Simulations in MATLAB were conducted to characterize this switching behavior using the identified noise parameters and using the identified noise parameters the aggregation of the swarm in both modes were identified. Our simulations demonstrate that with identified noise parameters that affect the particles motion, desired swarm aggregation can be achieved using simple robots that are capable of changing their geometric shape. This research highlights how the simplicity of hardware design of a single agent can achieve aggregations for swarms which enable various environmental sensing tasks to be achieved.
Canada Wildfire Next-Day Spread Prediction Tools Using Deep Learning
(University of Waterloo, 2024-08-14) Fang, Xiang
Wildfires have become a pressing issue globally, with their increasing frequency and intensity causing significant environmental, economic, and human impacts. Traditional wildfire prediction methods, while useful, often fall short in time complexity or simulation on heterogeneous landscapes. This thesis explores the application of deep learning models, especially convolutional networks, to improve the accuracy and reliability of wildfire spread predictions. By leveraging advanced machine learning techniques, this research aims to enhance the current prediction capabilities and provide better tools for Canadian wildfire management and mitigation. Utilizing a comprehensive dataset from various sources, this thesis integrates multiple features such as weather data, vegetation types, and topographical information. The research introduces a novel module for fusing multi-modal data, which enhances the performance of U-shape deep learning models like U-Net. Additionally, an innovative U-shape network structure with atrous(dilated) convolution and new attention implementation was developed to further improve prediction accuracy. The thesis also proposes an enhancement method that amplifies grouped error pixels for element-wise error computation for model training. The novel data fusion module proposed in this thesis has been proven to improve the baseline model on the F1 score, while the new model I suggest outperformed the baseline model and its two variants on the same metric. In the final part of the thesis I proposed various additional enhancement methods to improve performance further, it has shown its statistical significance under certain conditions when applied to BCELoss. By enhancing the predictive capabilities of wildfire spread models, this thesis offers valuable insights for emergency responders and policymakers, aiding in better resource allocation and risk mitigation strategies. The deep learning methodologies developed in this study are versatile and have potential applications in other fields requiring spatial data predictions, such as intelligent healthcare, flood forecasting, and disease spread modelling.
Exploring Power Fuzzing in Embedded Systems: Architecture, Challenges, and Enhancements
(University of Waterloo, 2024-08-14) Mehta, Kavish
Embedded Systems (ES) are becoming increasingly prevalent across various industries, playing an important role in everything from critical infrastructure to consumer electronics. However, their resource-constrained nature and complex interactions with the physical world make them susceptible to security vulnerabilities. Fuzzing, a technique that feeds random or mutated data to a program to uncover software bugs and vulnerabilities, has emerged as a powerful tool for improving embedded system security. This thesis explores the concept of power fuzzing, a specialized fuzzing approach that focuses on capturing variations in the power consumption of the Target System (TS) as feedback. We examine the power fuzzing structure, highlighting the different events triggered during fuzzing and the inherent variability associated with these events. The thesis also addresses challenges in data capture and the limitations of the Target System (TS). Furthermore, this thesis proposes two enhancements to improve the effectiveness of power fuzzing architectures: (1) Hardware Trigger and (2) Profile and Fine-Tune (PnFT) Approach. These enhancements aim to address the aforementioned challenges and contribute to a more robust security testing methodology for Embedded Systems (ES).

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