Memristive Devices with Multiple Resistance States Based on the Migration of Protons in α‐MoO<sub>3</sub>/SrCoO<sub>2.5</sub> Stacks
Zhe Wang, Heming Huang, Xin Guo
Abstract
Abstract Memristive devices are building blocks for neuromorphic computing. However, non‐ideal properties of memristive devices, such as bad retention, small number of resistance states, and nonlinear pulse programming hinder the development of neuromorphic computation. Based on proton migration in the α‐MoO 3 /SrCoO 2.5 stack, a Pt/α‐MoO 3 /SrCoO 2.5 /Nb‐SrTiO 3 memristive device is developed with multiple resistance states and excellent nonvolatility. When protons migrate from α‐MoO 3 to the SrCoO 2.5 lattice, both layers undergo a resistance increase, due to a reduced doping level in α‐MoO 3 along with the loss of protons, and a larger direct bandgap of SrCoO 2.5 resulted from the insertion of protons. While protons migrate from SrCoO 2.5 to α‐MoO 3 , the device resistance decreases, because of the increased proton concentration in α‐MoO 3 and the decreased proton concentration in the SrCoO 2.5 layer. The device also realizes nearly linear potentiation and depression under appropriate pulse schemes. A three‐layer backpropagation neural network constructed with the memristive devices acquires an accuracy of 94.3% for the recognition of MNIST handwritten digits.