Artificial SiN_z:H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing

Abstract

Inspired by its highly efficient capability to deal with big data, the brain-like computational system has attracted a great amount of attention for its ability to outperform the von Neumann computation paradigm. As the core of the neuromorphic computing chip, an artificial synapse based on the memristor, with a high accuracy in processing images, is highly desired. We report, for the first time, that artificial synapse arrays with a high accuracy in image recognition can be obtained through the fabrication of a SiNz:H memristor with a gradient Si/N ratio. The training accuracy of SiNz:H synapse arrays for image learning can reach 93.65%. The temperature-dependent I–V characteristic reveals that the gradual Si dangling bond pathway makes the main contribution towards improving the linearity of the tunable conductance. The thinner diameter and fixed disconnection point in the gradual pathway are of benefit in enhancing the accuracy of visual identification. The artificial SiNz:H synapse arrays display stable and uniform biological functions, such as the short-term biosynaptic functions, including spike-duration-dependent plasticity, spike-number-dependent plasticity, and paired-pulse facilitation, as well as the long-term ones, such as long-term potentiation, long-term depression, and spike-time-dependent plasticity. The highly efficient visual learning capability of the artificial SiNz:H synapse with a gradual conductive pathway for neuromorphic systems hold great application potential in the age of artificial intelligence (AI).

Keywords

• artificial synapse
• multilevel resistive switching
• silicon nitride
• Si dangling bond