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Evolution of conductive filament and its impact on reliability issues in oxide-electrolyte based resistive random access memory

The electrochemical metallization cell, also referred to as conductive bridge random access memory, is considered to be a promising candidate or complementary component to the traditional charge based memory. As such, it is receiving additional focus to accelerate the commercialization process. To c...

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Detalles Bibliográficos
Autores principales: Lv, Hangbing, Xu, Xiaoxin, Liu, Hongtao, Liu, Ruoyu, Liu, Qi, Banerjee, Writam, Sun, Haitao, Long, Shibing, Li, Ling, Liu, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4293596/
https://www.ncbi.nlm.nih.gov/pubmed/25586207
http://dx.doi.org/10.1038/srep07764
Descripción
Sumario:The electrochemical metallization cell, also referred to as conductive bridge random access memory, is considered to be a promising candidate or complementary component to the traditional charge based memory. As such, it is receiving additional focus to accelerate the commercialization process. To create a successful mass product, reliability issues must first be rigorously solved. In-depth understanding of the failure behavior of the ECM is essential for performance optimization. Here, we reveal the degradation of high resistance state behaves as the majority cases of the endurance failure of the HfO(2) electrolyte based ECM cell. High resolution transmission electron microscopy was used to characterize the change in filament nature after repetitive switching cycles. The result showed that Cu accumulation inside the filament played a dominant role in switching failure, which was further supported by measuring the retention of cycle dependent high resistance state and low resistance state. The clarified physical picture of filament evolution provides a basic understanding of the mechanisms of endurance and retention failure, and the relationship between them. Based on these results, applicable approaches for performance optimization can be implicatively developed, ranging from material tailoring to structure engineering and algorithm design.