Abstract
Resistive random access memories (RRAMs) are a type of resistive memory with two metal electrodes and a semi-insulating switching material in-between. As the persistent technology node downscaling continues in transistor technologies, RRAM designers also face similar device scaling challenges in simple cross-point arrays. For this reason, a cost-effective 3D vertical RRAM (VRRAM) structure which requires a single pivotal lithography step is attracting significant attention from both the scientific community and the industry. Integrating an extremely thin plane electrode to such a structure is a difficult but necessary step to enable high memory density. In addition, experimentally verifying and modeling such devices is an important step to designing RRAM arrays with a high noise margin, low resistive-capacitive (RC) delays, and stable switching characteristics. In this work, we conducted an electromagnetic analysis on a 3D vertical RRAM with atomically thin graphene electrodes and compared it with the conventional metal electrode. Based on the experimental device measurement results, we derived a theoretical basis and models for each VRRAM design that can be further utilized in the estimation of graphene-based 3D memory at the circuit and architecture levels. We concluded that a 71% increase in electromagnetic field strength was observed in a 0.3 nm thick graphene electrode when compared to a 5 nm thick metal electrode. Such an increase in the field led to much lower energy consumption and fluctuation range during RRAM switching. Due to unique graphene properties resulting in improved programming behavior, the graphene-based VRRAM can be a strong candidate for stacked storage devices in new memory computing platforms.
Highlights
With the progress in the field of deep learning and neural networks, the concept of computation is altering toward a data-centric paradigm [1,2]
Intensive operations required in such applications demand complex memory platforms
We focus on experimental verification and electromagnetic characterization of vertical Resistive random access memory (RRAM) (VRRAM) composed of either graphene or conventional metal electrodes and compare the results
Summary
With the progress in the field of deep learning and neural networks, the concept of computation is altering toward a data-centric paradigm [1,2]. Recent advances in non-volatile memory technology such as Flash were primarily focused on multilevel storage capability and transistor size reduction. Inherent drawbacks such as an increased bit-error rate and reliability degradation accompany such technology node downscaling [3,4]. Emerging memory devices with potentially higher performance and memory density compared to conventional devices can become an effective solution in today’s post-Moore era [5,6,7]. These technologies use conceptually different mechanisms of storing data, which are generally independent of the node size. With its simple metal–insulator–metal structure, extreme scalability and Complementary Metal–Oxide–Semiconductor (CMOS) compatibility are RRAM’s important attributes [12]
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