Controlled Formation of Conduction Channels in Memristive Devices Observed by X‐ray Multimodal Imaging

• Published in: Advanced materials (Weinheim) 2022-09, Vol.34 (35), p.e2203209-n/a
• Main Authors: Liu, Huajun, Dong, Yongqi, Galib, Mirza, Cai, Zhonghou, Stan, Liliana, Zhang, Lei, Suwardi, Ady, Wu, Jing, Cao, Jing, Tan, Chee Kiang Ivan, Sankaranarayanan, Subramanian K. R. S., Narayanan, Badri, Zhou, Hua, Fong, Dillon D.
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Channels; conduction channels; deterministic electroforming; Digital computers; Electric fields; Electrodes; Electroforming; Memory devices; memristive devices; Molecular dynamics; Neuromorphic computing; Oxygen; Spatial distribution; Stoichiometry; Synapses; Vacancies; X‐ray imaging
• ISSN: 0935-9648; 1521-4095
• DOI: 10.1002/adma.202203209

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software‐based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X‐ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. This work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing. While neuromorphic computing provides a means for achieving faster and more energy efficient computations, the accuracy needs improvement by reducing the randomness of memristive behavior. The controlled formation of conduction channels is shown here, as observed by X‐ray multimodal imaging of both oxygen stoichiometry and crystallinity. The results suggest an effective route for controlling the otherwise random electroforming process.