Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

Kaichen Zhu, Mohammad Reza Mahmoodi, Zahra Fahimi, Yiping Xiao, Tao Wang, Kristýna Bukvišová, Miroslav Kolíbal, Juan B. Roldan, David Perez, Fernando Aguirre, Mario Lanza

Research output: Contribution to journalArticlepeer-review


Memristive electronic synapses are attractive to construct artificial neural networks (ANNs) for neuromorphic computing systems, owing to their excellent electronic performance, high integration density, and low cost. However, the necessity of initializing their conductance through a forming process requires additional peripheral hardware and complex programming algorithms. Herein, the first fabrication of memristors that are initially in low-resistive state (LRS) is reported, which exhibit homogenous initial resistance and switching voltages. When used as electronic synapses in a neuromorphic system to classify images from the CIFAR-10 dataset (Canadian Institute For Advanced Research), the memristors offer ×1.83 better throughput per area and consume ×0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from ≈63% better density and ≈17% faster operation. It is demonstrated in the results that tuning the local properties of materials embedded in memristive electronic synapses is an attractive strategy that can lead to an improved neuromorphic performance at the system level.
Original languageEnglish (US)
Pages (from-to)2200001
JournalAdvanced Intelligent Systems
StatePublished - Mar 21 2022


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