Open AccessMemristors with Initial Low‐Resistive State for Efficient Neuromorphic Systems
Author(s) -
Zhu Kaichen,
Mahmoodi Mohammad Reza,
Fahimi Zahra,
Xiao Yiping,
Wang Tao,
Bukvišová Kristýna,
Kolíbal Miroslav,
Roldan Juan B.,
Perez David,
Aguirre Fernando,
Lanza Mario
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202200001
Subject(s) - neuromorphic engineering , memristor , initialization , computer science , artificial neural network , resistive touchscreen , process (computing) , electronic engineering , computer architecture , artificial intelligence , engineering , computer vision , programming language , operating system
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.