Analogue Artificial Synaptic Performance of Self‐Rectifying Resistive Switching Device
Hyun Kyu Seo, Jin Joo Ryu, Su Yeon Lee, Kanghyoek Jeon, Hyunchul Sohn, Gun Hwan Kim, Min Yang
Abstract
Abstract The immense increase of unstructured data require novel computing systems that can process the input data with low power and parallel processing. This functionality is similar to that of human brains that are composed of numerous neurons, synapses, and their complex connections. To mimic the functionality of the human brain with an electronic device, the resistive switching device and crossbar array has attracted considerable attention for artificial synaptic devices and integrated systems, respectively. For this purpose, the self‐rectifying resistive switching cell based on the Si:ZrO x thin film is developed and its reliability characteristics are tested. Four achievements are highlighted in this study. 1) The retention characteristic is improved by the adoption of TaO x thin film as an oxygen reservoir layer. 2) The asymmetric electrodes can make the self‐rectifying resistive cell (SRC) have sufficient rectifying characteristic. 3) The linearity of conductance update has a dominant effect on the inference performance compared to that of the conductance range variation. 4) The device of the interface‐type resistive switching shows a high enough device yield in the crossbar array device and exhibits reliable multiply‐and‐accumulate operations in the crossbar array to mimic the human brain‐inspired computing system.