Interdisciplinary Research Opportunities for Undergraduates in Semiconductor Technology 

Available Projects:

The following projects have been tentatively confirmed. More projects will be added to this list over time. The name of the faculty adviser is listed.

Atomic layer deposition (ALD) of dielectrics on 2D transition metal dichalcogenides (2DTMDs). The student will develop new ALD processes to deposit ultrathin dielectrics on 2DTMDs, without deteriorating the 2D-TMD electrical properties. (Ageeth Bol, Chemistry) 

Low-k dielectrics films as protective layers on photoelectrodes for artificial photosynthesis. The student will deposit ultrathin films and analyze charge-transfer reactions across chemically modified semiconductor electrode interfaces. (Stephen Maldonado, Chemistry)

Earth-abundant Phase Change Materials for Memories. Students will be involved in the structural prediction, synthesis and characterization of ternary and quaternary copper chalcogenides (Cu2[M]x[N]ySe4) and will also assist on sample preparation for optical and charge transport measurements. (Pierre Poudeu, Materials Science) 

Enhancing p-type Doping of GaN for Power Electronics. The student will pursue a novel approach for p-type doping consisting of focused-ion-beam (FIB) nano-implantation of Mg in GaN during epitaxy, followed by ion channeling studies of Mg incorporation. (Rachel Goldman, Materials Science) 

Atomistic simulations of the growth kinetics of III-N polytypes (such as in either zincblende vs. wurtzite) at the nanoscale. The students will apply first-principles calculations and kinetics Monte Carlo simulations to investigate the growth kinetics and polytype selections of Gallium Nitride, which is a wide bandgap semiconductor material with promising potential for power electronics, optical devices, and many other applications (Liang Qi, Materials Science)

Atomic-scale Imaging of Correlated Electron States. This project focuses on imaging low-dimensional materials for novel quantum devices. We are particularly interested in charge density waves and novel phase change memory. (Robert Hovden, Materials Science)

Simulation of thickness and lateral scaling trends of single domain ferroelectric bits in the nanoscale regime. The student will compute and simulate ferroelectric switching of monodomain ferroelectrics as the thickness and lateral size is scaled from 1 micron down to 10 nm to elucidate the influence of mechanical clamping and nucleation and growth regimes. (John Heron, Materials Science) 

Our group uses origami- and kirigami-based approaches to develop better-fitting, better-breathing, and better-filtering N95 class masks, miniature demonstrator PV modules, as well as novel sensor devices for physiological diagnostics and rehabilitation. This project involves a combination of mechanical modeling, laser- and 3D-additive fabrication, and testing of novel origami and kirigami structures, as well as monolithically integrated electronics for these applications. (Max Shtein, Materials Science).

Skin-Like Wearable Optoelectronics for Health Monitoring Students will work to design, optimize, and characterize stretchable LED devices and develop cutting-edge wearable health-care technology. (Xiwen Gong, Chemical Engineering)

Low-power nano-electronic circuits and their incorporation into artificial cells. Student will design and fabricate artificial ‘cells’, comprised of nano-electronic circuits connecting artificial ‘organelles’ (e.g., generators, sensors, logic gates, etc.), that combine the modularity of semiconductor electronics with the characteristic mobility found in dispersive biological systems. (Albert Liu, Chemical Engineering)

Development of electrochemical random-access memory for analog computing. The student will develop a new three-terminal nonvolatile memory cell that encodes analog resistance states using electrochemistry. (Yiyang Li, Materials Science) 

Memristive Devices Based on 2D Semiconductors (Xiaogan Liang, Mechanical Engineering)

Developing ferroelectric field effect transistors for next-generation computer memory. The student will study the fabrication and characterization of novel nitride ferroelectric based electronic devices to achieve ultralow power, ultrahigh speed, and nanometer scale memory devices essentially required for next generation computing. (Zetian Mi, ECE) 

Multiferroics for interference immune communication systems. The student will fabricate iron gallium (FeGa)/barium strontium titanate (BST) multilayers and characterize electric field control of ferromagnetic resonance for the design of electronically controlled radio frequency filters. (Amir Mortazawi, Electrical Engineering) 

High-k dielectrics for p-type oxide thin film transistors. The student will fabricate and characterize the interface properties and device reliability of p-type oxide thin film transistors that use high-k gate dielectrics to minimize the turn-on voltage.  (Becky Peterson, Electrical Engineering) 

A new type of semiconductor tactile sensor for robotics and artificial skins. This project explores the design, fabrication, characterization, and system integration of a new type of semiconductor tactile sensor that is essential for a robot to complete grasping, pose estimation, and item discovery tasks.  (P. C. Ku, Electrical Engineering)

Architectural exploration of new logic schemes. The student will study the trade-offs that arise when changing the strictly binary analog/digital boundary and will design an accelerator for sensor-oriented, specifically LIDAR, workloads. (George Tzimpragos, Electrical Engineering)

LiDAR Smart Targets. The student will first explore LiDAR technology for autonomous driving, its capabilities, and its limitations. They will then work with a LiDAR sensor to extract data and process it for imaging. The project then involves developing and testing engineered targets to study their impact on the LiDAR's measurement. (Aline Eid, Electrical Engineering)

Computer vision for feature identification in electron micrographs. The student will apply advanced computer vision algorithms to identify features in scanning electron micrographs. The datasets may be either semiconductor or metallurgy images. (Elizabeth Holm, Computer Science and Materials Science)

Accelerating big-data analytics applications. The student will develop skills to understand the performance bottlenecks of executing a class of big data analytics applications on a commercial hardware platform, and develop algorithm/software/hardware optimization techniques to speed up this application. (Nishil Talati, Computer Science)

Privacy-enhanced encrypted computation. The student will contribute to the development of privacy-enhanced encrypted computation capabilities and develop associated data-oblivious programming frameworks. (Todd Austin, Computer Science)

Architectural Acceleration of Explainable AI. The student will contribute to the architectural design and evaluation of accelerated platforms for explainable AI training and inference, including the development of explainable AI benchmarks. (Valeria Bertacco, Computer Science)