Thickness-Related Analog Switching in SiOx/Cu/SiOx Memristive Devices for Neuromorphic Applications

Abstract

This study examines the development of TiN/SiO_x/Cu/SiO_x/TiN memristive devices for neuromorphic applications using wedge-type deposition and Monte Carlo simulations. Identifying critical parameters for the desired device characteristics can be challenging with conventional trial-and-error methods, which often obscure the effects of varying layer compositions. By employing an off-center thermal evaporation method, a thickness gradient of SiO_x and Cu on a 4 inch wafer is created, facilitating detailed resistance map analysis through semiautomatic measurements. This approach allows for investigating the influence of layer composition and thickness while keeping other process conditions constant. Combining experimental data with simulations provides a precise understanding of layer thickness distribution and its impact on device performance. Optimizing the SiO_x layers to be below 12 nm, coupled with a discontinuous Cu layer with a nominal thickness under 0.6 nm, exhibits analog switching properties with an R_on/R_off ratio of >100, suitable for neuromorphic applications, while R × A and power exponent γ analysis show signs of multiple conduction mechanisms. The findings highlight the importance of SiO_x and Cu thickness in determining switching behavior, offering insights for developing high-performance analog switching components for bioinspired computing systems.