Yuriy V. Pershin

Yuriy V. Pershin is a Professor in the Department of Physics and Astronomy at the McCausland College of Arts and Sciences, University of South Carolina. His research focuses on emerging memory devices (e.g., memristors, memcapacitors), unconventional computing paradigms, and 2D materials like graphene. He leads experimental and theoretical investigations into device fabrication, nonlinear dynamics, and nanoscale phenomena.

Research Interests:

• Emerging Memory Devices: Designing memristive systems with memory retention capabilities, modeling their electrical behavior, and exploring their applications in low-power circuits.
• Unconventional Computing: Developing computing architectures that integrate memory and logic (e.g., neuromorphic networks), demonstrated through FPGA implementations and memristive neural networks.
• 2D Materials: Studying graphene kinks and antikinks as nanoscale motion carriers, with applications in nanoelectromechanical systems (NEMS).

Key contributions include defining rigorous tests for ideal memristors, optimizing Joule-loss reduction in memristive systems, and proposing hardware implementations of memcomputing. His work bridges fundamental physics with applied engineering, leveraging tools like SPICE modeling and molecular dynamics simulations.

Publications emphasize theoretical rigor and experimental validation, often addressing controversies in memristor characterization. Notable themes include noise-induced chaos in memcomputing, synchronization in memristive networks, and graphene-based electromechanical systems.

Awards and Grants: No specific awards listed, though his work reflects sustained research funding in nanotechnology and device physics. Collaborations span academia and industry, focusing on practical applications of novel materials and circuits.

Labs/Teams: His laboratory focuses on interdisciplinary projects at the intersection of physics, electronics, and materials science, with ongoing efforts in device fabrication, circuit emulation, and theoretical modeling.