Systematic Engineering of Metal Ion Injection in Memristors for Complex Neuromorphic Computing with High Energy Efficiency

Abstract

Neuromorphic electronics attract significant attention as a new computing architecture. Despite much effort for achieving practical neuromorphic systems, it is still challenging to construct a synapse array ideal for complex neural networks. Herein, a novel strategy for developing a highly integrated crossbar array of a one‐selector–one‐memory (1S–1R) synapse by systematically engineering ion injection is demonstrated. In the proposed synapse, an electrochemical metallization (ECM) memristor consisting of unstable filaments and a typical ECM device with stable filaments act as a selector with a low leakage current and a stable memory device, respectively. To overcome the voltage‐matching issues in constructing the 1S–1R synapse with high integration density, ion injection related with the electrical properties is optimized in the ECM devices via the distribution of active metal nanoparticles at the interface. The developed synapse possesses a high on/off ratio, superior selectivity, low operating current, and stable multilevel conductance, compared to the previously reported devices. High feasibility for complex neuromorphic systems is demonstrated, and the neural network based on the developed synapse array exhibits reliable parallel computation with high energy efficiency. This promising concept of realizing complex neuromorphic electronics is a fundamental building block for the practical artificial intelligence.

Keywords

• artificial synapses
• memristors
• neural networks
• one selector-one memory
• parallel computation