Hierarchically Ordered Nanoscale Electric Field Concentrators for Embedded Thin Film Devices
Stephen Nonnenmann
Resistance switching devices, also known as memristive devices, represent the next generation in computing. With a typical metal-oxide-metal structure, memristors change resistance under different external biases and retain this new resistance even when power is turned off. This allows memristors to store data without needing constant power like in traditional computer memory. Memristors have other desirable properties such as low power consumption, fast switching speed, and multistate logic potential. These properties open up next generation computing applications in non-volatile memory, reconfigurable switches, bio-inspired neuromorphic computing, and radiofrequency switches. However, before these applications are enabled, significant technical challenges in memristors must be overcome. These include cycle-to-cycle instabilities in operating voltage and resistance states, which cause memory retention and device endurance issues. Professor Stephen Nonnenmann and his laboratory address these instability issues by embedding highly ordered metal nanoislands in the memristor’s oxide switching layer. Through a unique template-directed nanoisland embedding procedure, the nanoisland diameter, spacing, and area density can be precisely controlled. The Nonnenmann lab found that through precise control of these variables, the growth of conductive filaments formed through the memristor’s oxide layer, which enable its unique properties, can be more precisely controlled, leading to a nearly 100% improvement in uniformity performance in one device case.
• Significant improvements in uniformity of resistance values, typically between 40-95% depending on the system • Reduce operating voltage and improve cycle uniformity • Template-directed embedment method very flexible and relies on known, lost-cost methods • Demonstrated embedment method with several metals (e.g. Ag, Pt, Ti) and can be applied to non-metallic materials
• Computer memory • Long term data storage • Neuromorphic computing • Functional composites • Photovoltaics
Stephen S. Nonnenmann is an Assistant Professor in the Department of Mechanical and Industrial Engineering at UMass Amherst. He previously served as a NBIC postdoctoral research fellow in the Department of Materials Science and Engineering at the University of Pennsylvania. He received his Ph.D. in Materials Science and Engineering from Drexel University, in 2010. His research focuses on clarifying the roles of interfacial phenomena, transport mechanisms, and nanoscale electromechanical coupling within oxide materials through direct, localized imaging via scanning probe microscopy methodologies under extreme environmental perturbation.
Available for Licensing and/or Sponsored Research
UMA 18-061
F
Patent Pending
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