All-Optically Controlled Memristive Reservoir Computing Capable of Bipolar and Parallel Coding

TL;DR

This paper presents an all-optically controlled memristive reservoir computing system with bipolar and parallel coding, significantly improving data processing capabilities and accuracy for edge computing applications.

Contribution

It introduces a novel optically controlled memristor array with wavelength-dependent bipolar response, enabling dynamic coding strategies for enhanced reservoir computing performance.

Findings

Bipolar photoresponse improves recognition accuracy.

Parallel coding enables multi-source signal fusion.

System demonstrates high stability and uniformity.

Abstract

Physical reservoir computing (RC) utilizes the intrinsic dynamical evolution of physical systems for efficient data processing. Emerging optoelectronic RC platforms,such as light-driven memristors, merge the benefits of electronic and photonic computation. However, conventional designs are often limited by the unipolar photoresponse of optoelectronic devices, which restricts reservoir state diversity and reduces computational accuracy. To overcome these limitations, we introduce an all-optically controlled RC system employing an oxide memristor array that demonstrates exceptional uniformity and stability. The memristive devices exhibit wavelength-dependent bipolar photoresponse, originating from light-induced dynamic evolution of oxygen vacancies. Tuning the power density and irradiation mode of dual-wavelength light pulses enables dynamic control of photocurrent relaxation and nonlinearity. By leveraging these unique device properties, we develop bipolar and parallel coding strategies to significantly enrich reservoir dynamics and enhance nonlinear mapping capability. In word recognition and time-series prediction tasks, the bipolar coding demonstrates markedly improved accuracy compared to unipolar coding. The parallel coding supports multi-source signal fusion within a single reservoir, maintaining high computational accuracy while significantly reducing hardware consumption. This work provides a high-performance approach to physical RC, paving the way for intelligent edge computing.