Abstract
Superior scalability, endurance, low power operation, retention, and operating speed of filamentary crystalline HfOx-based resistive random access memory (RRAM) makes this technology promising for implementation in exascale neuromorphic computing systems. Challenges, roadblocks for the implementation, and possible resolutions are discussed. Technological solutions to overcome RRAM variability (both device-to-device and cycle-to-cycle) and read instability are discussed. Major material properties and operation conditions controlling performance of the crystalline HfOx-based RRAM devices are linked to physical processes determining RRAM characteristics.
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