Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing
Jaehyun Kang, Tae-Yoon Kim, Suman Hu, Jaewook Kim, Joon Young Kwak, Jongkil Park, Jong‐Keuk Park, Inho Kim, Suyoun Lee, Sang‐Bum Kim, YeonJoo Jeong
Abstract
Abstract Memristors, or memristive devices, have attracted tremendous interest in neuromorphic hardware implementation. However, the high electric-field dependence in conventional filamentary memristors results in either digital-like conductance updates or gradual switching only in a limited dynamic range. Here, we address the switching parameter, the reduction probability of Ag cations in the switching medium, and ultimately demonstrate a cluster-type analogue memristor. Ti nanoclusters are embedded into densified amorphous Si for the following reasons: low standard reduction potential, thermodynamic miscibility with Si, and alloy formation with Ag. These Ti clusters effectively induce the electrochemical reduction activity of Ag cations and allow linear potentiation/depression in tandem with a large conductance range (~244) and long data retention (~99% at 1 hour). Moreover, according to the reduction potentials of incorporated metals (Pt, Ta, W, and Ti), the extent of linearity improvement is selectively tuneable. Image processing simulation proves that the Ti 4.8% :a-Si device can fully function with high accuracy as an ideal synaptic model.