Review of memristor devices in neuromorphic computing: materials sciences and device challenges

By Yibo Li, Zhongrui Wang, Rivu Midya, Qiangfei Xia, J. Joshua Yang. Journal of Physics D: Applied Physics, 2018

507 citations recorded by the source corpus.

The memristor is considered as the one of the promising candidates for next generation computing systems. Novel computing architectures based on memristors have shown great potential in replacing or complementing conventional computing platforms based on the von Neumann architecture which faces challenges in the big-data era such as the memory wall. However, there are a number of technical challenges in implementing memristor based computing. In this review, we focus on the research performed on the memristor material stacks and their compatibility with CMOS processes, the electrical performance, and the integration. In addition, recent demonstrations of neuromorphic computing using memristors are surveyed.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7f133591e022b5ab1b5abb0849eb716a83dc4659

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In Electrical and Electronic Engineering

1. J. Parker Mitchell
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Citations in the corpus

• Opportunities for neuromorphic computing algorithms and applications (2022, methodology): “One popular approach in the literature is using memristors as the fundamental device to have resistive memory to collocate processing and memory 31,32 , but other types of devices have also been used to implement neuromorphic computers, including optoelectronic devices 10 .”