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Controlling Ionic Transport in RRAM for Neuromorphic Computing Applications

Rapid advances of modern electronics based on device scaling are approaching fundamental limits and face severe power and cost constraints. Resistive random-access memory (RRAM), based on a simple two-terminal structure, has recently attracted tremendous interest for applications ranging from non-volatile data storage to neuromorphic computing. Resistive switching (RS) effects in RRAM devices originate from internal, microscopic ionic migration and associated electrochemical processes, which modify the materials’ composition and subsequently their electrical and other physical properties. Therefore, controlling the internal ionic transport and redox reaction processes, ideally at the atomic scale, is necessary to optimize the device performance for practical applications with large-size arrays. In this talk I will discuss our recent efforts in understanding and controlling the ionic processes in oxide-based RRAM devices. First, we investigated the fundamental electronic, optical, and structural properties of material and defect (e.g. oxygen vacancy (VO)) by first-principles calculations. Detailed theoretical and experimental analyses that reveal the dynamic VO charge transition processes will be discussed that can help understand the microscopic mechanism and optimize the RRAM devices. Next, we experimentally showed that the RS characteristics can be systematically tuned by inserting a graphene layer with engineered nanopores in which the graphene layer working as an atomically thin ion-blocking material, leading to improved device uniformity. Also, better incremental analog-switching characteristics were obtained through optimization of the switching layer density and stoichiometry. These improvements allow us to build RRAM crossbar networks for both neuromorphic applications and arithmetic applications such as K-means clustering in which accurate vector-matrix multiplications are required.

Jihang Lee

Department of Materials Science and Engineering, University of Michigan

Created March 5, 2018, Updated October 1, 2018