From Processing to Functionality: Engineering Accessible Material States in Cu-Embedded SiO$_x$ Memristive Devices

2026-09-24

AI Summary

This study addresses the lack of predictive correlations between fabrication conditions and functional behavior in oxide memristors by constructing a multiscale physics framework linking plasma deposition parameters to macroscopic device performance. Methodologically, it integrates large-scale statistical analysis, plasma and atomistic simulations, and data-driven clustering techniques to identify oxygen vacancy density as a critical latent variable. The core contribution lies in proposing a paradigm shift from deterministic defect engineering to probabilistic defect-state design. By revealing the underlying probabilistic cascade mechanism and elucidating how spatially heterogeneous subdomain integration drives variability in large-area devices, this work establishes a foundation for physics-informed, controllable memristor design.

Abstract

Resistive switching in oxide-based devices is widely governed by stochastic defect processes, yet a predictive link between fabrication conditions and functional behavior remains elusive. Here, we establish a multiscale framework connecting plasma-defined deposition conditions to macroscopic device functionality in sputtered SiO$_x$/Cu/SiO$_x$-based systems. By combining large-scale statistical analysis of more than 50,000 experimentally characterized devices with physics-based plasma and atomistic simulations, we show that device behavior does not emerge from deterministic process-to-performance mappings, but from a probabilistic cascade spanning defect formation, defect-state evolution, and functional-regime emergence. Data-driven clustering reveals a continuous functional state space composed of operational switching types, while inverse modeling identifies the reconstructed oxygen-vacancy density as an effective latent descriptor capturing the combined influence of structural disorder and defect topology. This latent descriptor is strongly coupled to both Cu redistribution and electrical response, linking otherwise hidden material properties to observable device characteristics. Furthermore, macroscopic switching behavior is argued to arise from ensemble integration across spatially heterogeneous subdomains, providing a physical explanation for the pronounced variability of large-area devices. These findings shift the perspective from deterministic defect engineering toward probabilistic defect-state design and establish a physically grounded framework for understanding and controlling functional variability in such oxide-based systems, such as memristive or resistive-switching devices.

Problem

• memristive devices
• resistive switching
• functional variability
• defect engineering
• SiO$_x$

Innovation

• memristive devices
• multiscale framework
• probabilistic defect-state design
• latent descriptor
• resistive switching