Wedge-type engineered analog SiO$_\mathrm{x}$/Cu/SiO$_\mathrm{x}$-Memristive Devices for Neuromorphic Applications

TL;DR

This paper develops and analyzes wedge-type SiOx/Cu memristive devices with optimized layer thicknesses for neuromorphic computing, combining experimental and simulation methods to understand and improve their analog switching properties.

Contribution

It introduces a novel wedge-type deposition technique and combines experimental data with simulations to systematically optimize memristor layer parameters for neuromorphic applications.

Findings

Achieved analog switching with R_on/R_off > 100

Identified optimal SiOx thickness below 12.5 nm

Discontinuous Cu layer below 0.6 nm enhances performance

Abstract

This study presents a comprehensive examination of the development of TiN/SiO$_\mathrm{x}$/Cu/SiO$_\mathrm{x}$/TiN memristive devices, engineered for neuromorphic applications using a wedge-type deposition technique and Monte Carlo simulations. Identifying critical parameters for the desired device characteristics can be challenging with conventional trial-and-error approaches, which often obscure the effects of varying layer compositions. By employing an \textit{off-center} thermal evaporation method, we created a thickness gradient of SiO$_\mathrm{x}$ and Cu on a 4-inch wafer, facilitating detailed resistance map analysis through semiautomatic measurements. This allows to investigate in detail the influence of layer composition and thickness on single wafers, thus keeping every other process condition constant. Combining experimental data with simulations provides a precise understanding of the layer thickness distribution and its impact on device performance. Optimizing the SiO$_\mathrm{x}$ layers to be below 12.5 nm, coupled with a discontinuous Cu layer with a nominal thickness lower than 0.6 nm, exhibits analog switching properties with an R$_\mathrm{on}$/R$_\mathrm{off}$ ratio of $>$100, suitable for neuromorphic applications, whereas R $\times$ A analysis shows no clear signs of filamentary switching. Our findings highlight the significant role of carefully choosing the SiO$_\mathrm{x}$ and Cu thickness in determining the switching behavior and provide insights that could lead to the more systematic development of high-performance analog switching components for bio-inspired computing systems.