This project will introduce the REU student to modeling and simulation of photonic neural networks, an emerging computing platform that uses light to perform brain-inspired information processing. The student will work with graduate students to study how learning algorithms, photonic devices, and optical circuits interact in large neuromorphic systems. In current simulations, photonic neurons based on phase-change materials (PCMs) are often treated as fixed components whose behavior does not change with the input signal, making it difficult to realistically model learning and adaptation in large networks.

In this project, the student will help develop an integrated simulation approach that links learning algorithms with device-level behavior and circuit-level signal propagation, allowing the network response to evolve during computation. Through this work, the student will gain hands-on experience with photonic circuit simulation tools, basic numerical modeling, and concepts in neuromorphic and optical computing. The student will work closely with graduate students and receive direct mentorship from the research team throughout the project. Prior experience with Python, MATLAB, or Lumerical is encouraged.

This project will engage the REU student in hands-on experimental research focused on nanoimprint lithography, a scalable nanofabrication technique for creating nanoscale patterns over large areas. Working closely with a graduate student mentor, the student will help develop and optimize processing recipes on a nanoimprinting tool to pattern both polymer films and metal-halide perovskite thin films. The project will involve exploring imprint conditions such as temperature, pressure, and imprint time, as well as evaluating pattern fidelity and film quality after imprinting.

Through this work, the student will gain practical experience in nanofabrication, thin-film materials processing, and cleanroom-based experimentation. The student will learn how nanoscale patterning influences material and device behavior and will be trained in safe and reproducible laboratory practices. No prior nanofabrication experience is required; close mentorship and hands-on training will be provided throughout the project.

This REU project focuses on the experimental characterization of III-V/Si quantum dot micro-ring devices for generating various optical logic functions such as AND, OR, XOR, etc. The project’s goal is to evaluate the performance of these devices under high-speed modulation to optimize their application for photonic computing

Project Objectives:

• Write lab automation software for basic device testing.
• Conduct high-speed testing of III-V/Si quantum dot devices, including small-signal and large-signal modulation analysis.
• Demonstrate logic AND, OR, XOR operations.
• Analyze the impact of temperature, bias conditions, and yield.
• Compare experimental results with theoretical models to improve device design.

This REU project focuses on the design of Flash memory co-integrated with III-V/Si devices. The project’s goal is to evaluate different Flash memory dielectric stacks and the impact on non-volatile optical operations.

Project Objectives:

• Identify candidate flash memory dielectric stacks.
• Conduct design and simulation (using Lumerical, Silvaco, Synopsys) of these structures and benchmark performance such as non-volatile retention, power consumption, optical losses, etc.
• Compare experimental results with theoretical models to improve device design.

Electronic packaging refers to the design and assembly of electronic components and systems into a physical structure that protects them from environmental and mechanical damage while ensuring electrical connectivity and thermal management. It plays a crucial role in maintaining the performance, reliability, and manufacturability of electronic devices, especially in power electronics applications such as silicon carbide (SiC) power modules.

Traditional packaging design relies on iterative simulations and empirical testing, which can be time-consuming and cost-intensive. This project aims to leverage artificial intelligence (AI) and machine learning (ML) techniques to optimize the co-design of electronic packaging, integrating multiphysics considerations such as thermal management, mechanical reliability, and electrical performance. AI/ML-driven models will be developed using high-fidelity simulation and experimental data to accelerate design space exploration and predictive analysis. A digital twin framework will be incorporated to continuously refine predictions using real-time data, improving the adaptability of the design process.

To validate the AI/ML-based optimization framework, experimental studies will be conducted. These include power cycling tests to assess thermal and mechanical fatigue in interconnects and solder joints, as well as reliability tests to evaluate long-term performance under thermal, electrical, and mechanical stress conditions. The experimental results will be used to further refine and validate the predictive models, ensuring robust and manufacturable packaging solutions.

By integrating AI/ML with physics-based simulations and experimental validation, this research aims to reduce design iteration cycles, enhance operational efficiency, and extend the lifetime of power electronic modules. The outcomes will benefit industries focused on power electronics, automotive applications, and semiconductor manufacturing by providing a data-driven, automated approach to electronic packaging co-design.

This project aims to explore how electromagnetic fields, particularly microwave frequencies, interact with different materials and potentially influence chemical reactions. The primary goal is to understand how microwave energy can initiate, accelerate, or control both gaseous and solid-state reactions. A variety of experimental techniques will be employed to characterize these interactions, including:

• RGA (Residual Gas Analysis) for studying reaction products and gas-phase dynamics.
• XRD (X-ray Diffraction) to analyze the lattice structure of materials before and after exposure.
• Raman Spectroscopy to monitor vibrational modes and molecular changes.
• UV-Vis Spectroscopy for probing optical properties and reaction kinetics.
• Transmission Electron Microscopy (TEM) to observe material morphology and microstructure at the nanoscale.
• Vibrating Sample Magnetometry (VSM) and AC Magnetic Susceptibility (ACMS) for examining magnetic properties.
• Electrical conductivity measurements to assess changes in electrical behavior due to EMF exposure.

By combining these techniques, the project will provide detailed insights into the role of microwave fields in material transformation and chemical reactions.

This project focuses on the synthesis of novel thermoelectric materials with spin-driven properties. The goal is to design and produce materials that exhibit enhanced thermoelectric performance for energy conversion applications. Key synthesis techniques will include nanopowder preparation, spark plasma sintering, and wafer slicing and dicing to fabricate the thermoelectric materials.

The materials will be extensively characterized to investigate their thermal, electrical, magnetic, and thermoelectric properties. The following experimental methods will be employed:

• X-ray Diffraction (XRD) to analyze the lattice structure and phase composition of the materials.
• Raman Spectroscopy to monitor vibrational modes and molecular changes that may affect thermoelectric performance.
• UV-Vis Spectroscopy to probe optical properties and study reaction kinetics during material processing.
• Transmission Electron Microscopy (TEM) to examine material morphology and microstructure at the nanoscale.
• Vibrating Sample Magnetometry (VSM) and AC Magnetic Susceptibility (ACMS) to study magnetic behaviors that may influence thermoelectric efficiency.
• Electrical conductivity, Hall coefficient, Nernst coefficient, thermal conductivity, and thermopower measurements to evaluate the materials’ thermoelectric performance and determine their suitability for practical applications.

Through these techniques, the project aims to develop efficient thermoelectric materials for energy harvesting applications.

Students will work on research projects focused on Wide-bandgap (WBG) semiconductors. WBG Semiconductors like Silicon Carbide,and Gallium Nitride enable electronic devices to operate at much higher voltages, frequencies, and temperatures more efficiently than silicon. These technologies are great for defense and civilian applications where Size, Weight, and Power (SWaP) matter, allowing more electronics on airplanes and satellites, electric vehicles that go further on a battery charge, more powerful and accurate Radars, and faster communication systems. Emerging ultra-wide bandgap semiconductors like Diamond and Gallium Oxide can operate at even higher voltages and may be part of shipboard systems and future electric power grids. Students that participate in this program will work on a variety of materials fabrication and/or characterization techniques with these materials as well as receive professional development and networking opportunities.

Students will work on research projects focused on Wide-bandgap (WBG) semiconductors. WBG Semiconductors like Silicon Carbide,and Gallium Nitride enable electronic devices to operate at much higher voltages, frequencies, and temperatures more efficiently than silicon. These technologies are great for defense and civilian applications where Size, Weight, and Power (SWaP) matter, allowing more electronics on airplanes and satellites, electric vehicles that go further on a battery charge, more powerful and accurate Radars, and faster communication systems. Emerging ultra-wide bandgap semiconductors like Diamond and Gallium Oxide can operate at even higher voltages and may be part of shipboard systems and future electric power grids. Students that participate in this program will work on a variety of materials fabrication and/or characterization techniques with these materials as well as receive professional development and networking opportunities.