Emerging threats in modern processor design underscore the need for robust hardware security measures, particularly in scenarios where unintended information leakage can compromise sensitive data (Kocher et al.; Lipp et al., 2020). Information Flow Tracking (IFT) has become a critical technique to enforce confidentiality and integrity requirements by identifying unintended data propagation paths (Hu et al., 2022). However, existing IFT approaches often introduce considerable overhead, limiting their applicability in performance- and cost-sensitive domains.

In this work, we propose a custom IFT solution integrated into a constant-time multiplier for the Rocket Core. By engineering the taint propagation logic to address data correlations and reconvergence conditions, our approach reduces unnecessary complexity while preserving security guarantees. We compare our implementation to both CellIFT (Solt et al.) and a self-composition method (SPV, JasperGold) to assess relative trade-offs in precision, performance, and resource utilization. Formal verification results demonstrate a remarkable improvement in tracking precision, reducing false positives compared to prior approaches. Furthermore, our module design yields approximately 24% as many gates as CellIFT, offering tangible cost and area benefits. Compared to SPV, we observe 21% fewer overall flip-flops, making our solution attractive for resource-constrained hardware applications. This work presents an approach for reconvergence-informed IFT within an open source RISC-V processor, providing new insights into efficient hardware taint tracking and reinforcing the viability of IFT for secure processor architectures.

Drone-based item delivery has thus far in its short but innovative history revolved around a multi-rotor, miniature aircraft, performing a full landing to deliver packages. This system carries a range of technical and structural problems, including relatively poor flight dynamics, high battery power consumption, and range limitations. This thesis explores the deployment of electronically guided Smart Parachutes for drone-based package delivery, allowing the drone to perform a ‘fly-by’ during which it ejects the target’s package instead of the delivery requiring a full landing. Successfully designing and building a Smart Parachute delivery system repairs a wide range of social ills by adding yet more efficiency to the drone delivery network. Modern society suffers severe automotive congestion, pollution, theft, and latency between customer order and customer product receipt. A scaled drone delivery system mitigates the vast majority of these ills. As such, the project is worth pursuing.

The thesis specifically will explore a number of electromechanical solutions, highly reliant on granular, software-driven, location-sensing (via camera feed), as well as optimizing GPS coordination. These measurements will then feed into a rapid system state update algorithm to plan an optimal path for the Smart Parachute. Lastly, the system state algorithm will control a servo motor to steer the rigging of the parachute and update the feedback loop.

This project involves constructing an apparatus that mounts to a standard player piano mechanism in place of the paper piano roll and allows MIDI files to be played on the instrument. While modern player retrofit kits exist using solenoids, these need to be directly installed onto the piano action by an experienced technician. Additionally, installing such a kit on a player piano necessitates removing most, if not all of the original player mechanism. For my project, I aim to construct a portable device that can be easily installed by any individual on any functioning player piano. This will not only reduce the cost and effort required to update the piano, but it will also preserve its originality whilst giving it the ability to play MIDI files.

Though hardware accelerators decrease the operational carbon emissions of contemporary systems-on-chip (SoCs), their significant area, and thus embodied carbon cost, makes their impact on the overall lifetime sustainability of a chip less clear. As opposed to instantiating separate hardware for each accelerated application kernel, time sharing hardware resources for multiple kernels can decrease the total chip area required for acceleration, at the cost of increased energy consumption. This paper examines a spectrum of methods for this hardware sharing, in order to find the architectures which optimally trade off operational and embodied carbon emissions.

Our analysis quantifies the carbon impact of sharing for both compute and memory hardware. We show that the decreased area and power consumption which comes with highly specialized compute hardware outweighs the benefits of sharing compute hardware with a reconfigurable fabric. Importantly, however, the power and area overhead required to share memory hardware is low, and so sharing memory comes with a substantial carbon-efficiency improvement. Hence, the best option is fixed-function accelerators which keep compute separate but maximally share on-chip memory hardware. Our analysis also shows sustainable sizing options for tailoring these shared memory pools to the memory needs of an application's accelerator collection.

In the Radio Frequency Integrated Circuit design industry today, the design process is both complex and labor-intensive, demanding deep domain expertise and significant time investment. A designer first starts with target performance specifications. After establishing a general architecture, the designer then chooses topologies for each gain stage, iteratively adjusting parameters until the active portion of a circuit is produced. Then, the designer must match the impedances of each stage, utilizing predefined parameter sweeps and heuristic techniques to adjust and optimize their passive component designs. This process is extremely tedious, taking anywhere from a few weeks to several months depending on the complexity of a design. To address this issue and expedite the design process, this thesis tackles the development of both passive and active components by utilizing machine learning methods. Specifically, we utilize inverse design methods for passive structures and reinforcement learning for active components to synthesize power amplifier circuits from end-to-end algorithmically. Moreover, we consolidate these tools into graphical user interfaces to provide a ready-to-use product for RFIC design engineers anywhere.

The rapid emergence of advanced therapeutics has highlighted the need for innovative drug delivery systems (DDSs) capable of overcoming biological barriers and ensuring precise spatial and temporal control. This work focuses on the design and simulation of a dual-mode microfluidic DDS that combines electrochemical and magnetic actuation mechanisms to enable reliable and minimally invasive therapeutic delivery. Computational simulations demonstrate the system’s ability to maintain laminar flow under varying conditions, including different Reynolds numbers, fluid viscosities, and multi-input configurations, ensuring robust performance across diverse scenarios. Guidelines for fabrication have been established, detailing the integration of electrochemical drug release via gold membrane dissolution and a magnetic fallback system using iron-doped PDMS membranes for power-independent operation. The front-end fluid control system has been physically implemented on the macro-scale and addresses critical challenges in localized drug delivery by combining spatiotemporal precision, adaptability, and robustness.

In light of the tremendous possibilities offered by waveform design in the near field, we develop simulations of a sub-THz radar system that senses using Airy beams. Remarkably, this class of waveforms is “self-accelerating”: they trace out parabolic trajectories in free space in the absence of external forces. As such, a radar equipped with such a beam should be capable of around-the-corner sensing. This newfound capability opens entirely new dimensions in traffic sensing (among others), and its deployment in traffic radar may lead to significantly improved safety outcomes. Our central contribution lies in determining that conjugating the complex E-field incident on an obstacle (via an active surface) generates an Airy beam that returns to the transceiver plane by tracing the path taken by the original. This is true regardless of obstacle orientation. We also investigated the effect of (vertical) obstacle length on the link-budget. As expected, the percentage of returned power increases with obstacle length, reaching a plateau at ~ 80%. We determine that an ideal obstacle length might be in the vicinity of 0.4 (twice the aperture size of 0.2), since it provides a robust middle ground between returned power and size. In the same line of inquiry, we also found that the smallest obstacle length should be about 0.05 (a quarter of the aperture size). At this limit, an (unconjugated) specular reflection might, in fact, yield greater power returns in the primary lobe than conjugation.

As computing evolves and the use of cloud environments increases, there is growing concern over security and data confidentiality. Virtual machine-based trusted execution environments (VM-based TEEs) have emerged as a promising solution due to their flexibility and scalability. This thesis presents a formal verification framework for reasoning about the correctness of the Intel TDX firmware—a central component of a prominent VM-based TEE. Using CBMC, a suite of abstraction strategies, and modular verification reasoning, the framework enables tractable analysis of this complex, real-world system. The methodology is specifically applied to the TD creation sequence, demonstrating that the verification process successfully validates correct flows and reliably detects errors when deviations occur. Beyond TDX, this work contributes to the broader goal of developing a scalable and rigorous methodology for evaluating secure processor architectures and ensuring their reliability in real-world deployments.

Most robotics systems currently in use rely on either an internal battery that is intermittently charged, or outside cables to provide power to local systems. As robots become smaller and approach micrometer or nanometer scales, sustaining battery and circuit technology to fit these size constraints become increasingly challenging. Magnetic origami robots give two major benefits. By integrating permanent magnetics within origami structures, magnetic fields can be used to extend or contract these robots and achieve both control and power transmission wirelessly. My research involves a twofold approach of both mechanical design and robotics testing. The first section of my Senior Thesis centers around constructing a 3-dimensional Helmholtz coil project that can manipulate magnetic objects such as foldable origami robots without direct contact. It will improve upon the existing device in use by providing a larger workspace volume inside the coil while generating an equally strong magnetic field of 60 mili-Teslas. The second section of my Senior Thesis revolves around using the current coil system to explore the resonance behavior of origami robots based on the Kresling origami cell. This is achieved by applying a continuous rotating magnetic field. At specific frequencies, we can cause these Kresling structures to collapse or expand through oscillation of a permanent magnet attached to the robot In this way, the robot can be actuated while consuming less power compared to a conventional static magnetic field approach. Because the resonance behavior is dependent on the material properties of the Kresling Robots, this research can pave the way for future research on isolated control of these structures across a variety of applications.

The performance of GaAs-based electronic and optoelectronic devices is critically shaped by surface morphology and interface roughness—both of which are strongly influenced by Molecular Beam Epitaxy (MBE) growth conditions. This project investigates two key questions: (1) how specific MBE parameters—including step-flow growth and native oxide desorption— influence surface features; and (2) how these features impact directional electrical transport, particularly mobility anisotropy. Atomic Force Microscopy (AFM) was used to characterize roughness, step orientation, mounding behavior, and pit formation on GaAs(001) surfaces. We find that well-controlled vicinal growth produces uniform step-flow terraces when angled toward (111)A, with more ragged steps when angled toward (111)B. We demonstrate that poor oxide desorption, arsenic deficiencies, or excess gallium leads to substantially rougher surfaces. A ramped preheat protocol was developed to balance oxide removal with minimal surface damage. We correlated AFM surface metrics with transport properties in quantum well heterostructures, demonstrating the influence of interface roughness on mobility anisotropy. Additionally, spatial frequency anisotropy in AFM scans correlated with directionally dependent carrier mobility. Together, these results clarify how surface features arise from MBE conditions and directly impact transport behavior, offering a pathway toward more precise engineering of GaAs.

The fast-evolving nature of malware calls for the development of detection tools that work on attacks that were previously unseen. MalConv, a static classifier built on a convolutional neural network, is a significant step in this direction, but is unable to provide mathematical guarantees of its accuracy on its own. In this project, techniques that defend image classifiers from localized adversarial example attacks and calculate certified accuracy are applied to malware classifiers. In particular, De-Randomized Smoothed MalConv, an existing application of an image-based technique with a small receptive field, is extended for better performance on small files in models I call DRSM2 and PCM. DRSM2 improves DRSM to better utilize its base classifiers for small inputs; PCM applies PatchCleanser, an image-based technique with a large receptive field, to malware detection. Both models outperform the original DRSM, with DRSM2 achieving higher standard and certified accuracies but PCM providing certified accuracies for big perturbation sizes that DRSM2 cannot handle.

Memristors, first hypothesized by Leon Chua as the fourth fundamental circuit element, offer notable applications for in memory computing due to their analog switching properties and non-volatile behavior. Photonic memristors, in particular, are a breakthrough technology that offer ultrafast switching and superior energy efficiency. Yet, to date, these devices have predominantly relied on thermally driven phase change materials (PCMs), limiting integration density and resulting in degradation. This thesis pioneers the simulation and design of the first photochromic-actuated photonic memristor, extending the foundational work in ‘Dynamics of A Photochromic-Actuated Slot Microring Photonic Memristor. By leveraging reversible isomerization in diarylethene molecules confined within a slot microring resonator, the device achieves switching governed purely by photon exposure, effectively eliminating thermal bottlenecks.

A full stack simulation framework was developed using Tidy3D and Python to model photoinduced state dynamics. This work verifies the three canonical fingerprints of memristive behavior, including hysteresis loop formation, area decay above a critical threshold observed at 5 Hz, and eventual collapse into a quasi-linear regime. This algorithm also performs radius tuning to optimize memory, yielding an optimal length of 104.3 microns, and verifies that hysteresis can arise from pure phase modulation without reliance on lossy mechanisms. Beyond design, this thesis creates several original optimization techniques: a dual-wavelength waveguide enabling co-confinement of UV and visible light to eliminate bulky flood illumination, and the construction of a novel photochromic neurosynaptic network. Novel integration strategies–such as quantum-dot enhanced light delivery–and molecular engineering via a custom ternary phase diagram of chemical substituents, further extend the design space. Translating theoretical equations into physically simulated architectures shows that the memristor is a viable method of high-performance, on-chip photonic computing.

Drones are increasingly employed in critical applications, yet ensuring their safe operation in dynamic and unpredictable environments remains a challenge. This thesis examines the use of reach-avoid reinforcement learning (RL) for developing a task-agnostic safety filter for drones, with a focus on theoretical guarantees, practical applications, and future directions. By integrating safety constraints directly into the learning process, reach-avoid RL offers a robust and scalable framework for navigating the complexities of real-world safety scenarios. The reach-avoid safety filter, in combination with deep reinforcement learning and game-theoretic approaches, offers a feasible method for safe reinforcement learning across a range of tasks and environments in drone deployment.

In Wireless Communication System, Line-of-Sight (LOS) paths between the transmitter and receiver are often obstructed by trees, walls, buildings, and other obstacles. Reconfigurable Intelligent Surfaces (RIS) offer a promising solution by capturing these blocked signals and intelligently reflecting them toward the intended User Equipment (UE). In such scenarios, the user may be stationary or mobile, and there may be one or multiple users communicating with the same base station (BS). Accurate signal reflection relies on knowing the user’s angle, which is often unknown due to environmental blockages or mobility. To address this, I developed two beam training methods - Exhaustive and Hierarchical - to localize a single static user. Additionally, I proposed two scalable techniques - a correlation-based compressive sensing method and a neural network regression model - to track multiple users simultaneously, whether fixed or mobile. These techniques are shown to be effective in both noiseless and noisy environments, with real-world relevance in Vehicle-to-Everything (V2X) communication, IoT, and mobile networks.

Electrification plays a significant role in combating climate change, and the electrification of aviation has been a challenge, particularly because of the strict power density requirements of the power and propulsion systems in electric aircraft. Recently, hydrogen-fuel celled planes have been a promising solution to this problem, but two of the major obstacles with this new technology are various voltages needed on an airplane and the cryogenic temperatures involved with liquid hydrogen. One critical voltage level needed is a low voltage 28VDC bus that is used to power all the avionics on an aircraft and will be the objective of this two part thesis. The first part of this thesis details the construction and design decisions of a double pulse test testbench to observe and analyze the hard switching power loss and the dynamic resistance characteristics at various drain source voltages of cryogenically cooled Gallium Nitride transistors. The data and information gathered from the test is analyzed and used for the second part of the thesis to build a prototype converter that is capable of converting the output from a typical aircraft-sized hydrogen fuel cell (250VDC) to the aircraft standard low voltage (28VDC) that has features optimized for cryogenic temperatures. The novel contributions of this thesis involve investigating the dynamic resistance behavior of pure enhancement-mode GaN transistors at cryogenic temperatures and the construction of a cryogenic power converter based on pure enhancement-mode GaN transistors.

The manufacturing of affordable pitch calling systems is imperative for providing equal opportunity for all college baseball teams to compete with the wide ranges in team budgets. In this project, I aimed to produce a reliable alternative to the industry leader for under 10% of the cost. I did so using microcontrollers with transceiver capabilities with an attached LCD screen. By transmitting via BLE, there is much less power consumption, which means a longer battery life. This independent project is meant to offer college baseball teams a chance to save valuable funds that would otherwise be effectively wasted. Ideally, these teams would invest the saved funds on more productive ways to improve their team, thus improving the overall level of play in college baseball.

This thesis explores the development of an omnidirectional treadmill for enhancing movement within virtual reality (VR) environments. Traditional VR movement methods, like joystick navigation or teleportation, often lead to motion sickness or reduced immersion due to their unnatural mechanics. The omnidirectional treadmill presents a promising solution by allowing natural, 360-degree movement without the spatial constraints of the physical world. This thesis explores existing commercial solutions, including the Virtuix Omni and Infinadeck, and attempts to replicate a novel design inspired by Disney's Holotile technology. This new design utilizes motorized cones to move a user in 360 degrees, enabling the capability to keep them centered on the treadmill while they walk, thereby enhancing the VR experience while maintaining a compact and flexible system. The thesis details the design process, challenges, and iterative improvements of the treadmill and accompanying software.

Language acquisition and reasoning, two skills fundamental to human intelligence, both require internalizing and applying a collection of rules and structures derived from a limited set of examples. Crucially, this requires not only the ability to identify important structures in those examples, but also the ability to systematically combine learned structures in novel combinations, or systematicity. Interestingly, despite recent successes in the sphere of language generation, statistical learners like today's foundation models still fail to consistently reason systematically, suggesting a possible incompatibility between the current learning paradigm or model architectures and the acquisition of systematicity. We aim to develop a better understanding of the extent to which statistical learners are capable of generalizing to novel combinations, which we call compositional generalization, in systematic tasks. To that end, we identify some ways in which model architecture or optimization choices can encourage the inductive biases needed for models to generalize compositionally, as well as challenges in more realistic task settings.

Beam steering is a technique that controls the direction of a beam of radiation as it travels from transmitter to receiver, allowing for focused delivery of energy to certain target locations. In the case of wireless communication, this allows a transmitter to compensate for the path loss signal attenuation it encounters when emitting over the air. Terahertz (THz) and sub-THz radiation—electromagnetic emittance ranging from 10 GHz to 10 THz—is considered to be the next frontier of wireless communication given its ability to provide much higher data speeds and support a larger user capacity compared to current networks. Additionally, sub-THz has gained significant traction in imaging and biomedical applications given its small wavelengths—which provide high sensing resolution—and high sensitivity to relevant biomarkers. Furthermore, the development of reliable means of controlling THz radiation is necessary for the development of these THz communication, imaging, and biomedical diagnostic systems. In this paper, we present a metasurface fabricated with a 2D array of C-shaped single split-ring resonators (SRRs) that when linearly polarized radiation at sub-THz (60 GHz) frequency is incident on the C-shaped single split-ring resonator metasurface, the radiation will be steered at some predetermined angle according to the metasurface’s C-shaped single split-ring resonator architecture. We show that this beam steering can be turned off via thin films of a depletion mode organic semiconductor such as Poly(ethylene dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and remains intact with thin films of accumulation mode organic semiconductors such as pG(2T-3T). Going forward we aim to apply these insights to make a reconfigurable metasurface whose beam steering properties can be turned on and off, likely by turning organic electrochemical transistors placed over the resonator gap openings and fabricated with PEDOT:PSS off and on respectively.

Layered transition metal dichalcogenides are an emerging class of atomically-thin, layered materials for use in solar cells, transistors, LEDs, and other optoelectronic devices. These materials are flexible and durable, with bandgaps that shift from indirect as a bulk film to direct as a monolayer, allowing for finely tunable devices. Two of these materials, WSe2 and WS2, have bandgaps around 2 eV, making them more versatile than other 2D materials such as graphene, which lacks a bandgap. This project focused on mono- and multilayer WSe2 and WS2 films, and determined their conductivity, established the position of the Fermi level, and introduced [RuCpMes]2, an air stable dimer, as an n-type dopant to determine shifts in Fermi level and changes in conductivity with doping. The introduction of the Ru dimer increased the conductivity of monolayer WSe2 films by 3 orders of magnitude and increased the conductivity of monolayer WS2 films by 4 orders of magnitude, indicating successful doping. The Fermi level of undoped monolayer WSe2 was found to be close to the conduction band minimum and the Fermi level of monolayer WS2 was found to be slightly below the conduction band minimum. After doping with [RuCpMes]2, the Fermi level of monolayer WSe2 did not shift significantly due to its proximity to the valence band minimum, but the Fermi level of monolayer WS2 shifted upward. Due to charges on the surface, the work function of both monolayer films decreased with doping, with a greater change in monolayer WS2.