All-Optically Controlled Memristive Reservoir Computing Capable of Bipolar and Parallel Coding
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908833490665472 |
|---|---|
| author | Hu, Lingxiang Jiao, Dian Wang, Kexuan Cheng, Peihong Wang, Jingrui Zhang, Li Vasilakos, Athanasios V. Chai, Yang Ye, Zhizhen Zhuge, Fei |
| author_facet | Hu, Lingxiang Jiao, Dian Wang, Kexuan Cheng, Peihong Wang, Jingrui Zhang, Li Vasilakos, Athanasios V. Chai, Yang Ye, Zhizhen Zhuge, Fei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12938 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | All-Optically Controlled Memristive Reservoir Computing Capable of Bipolar and Parallel Coding Hu, Lingxiang Jiao, Dian Wang, Kexuan Cheng, Peihong Wang, Jingrui Zhang, Li Vasilakos, Athanasios V. Chai, Yang Ye, Zhizhen Zhuge, Fei Applied Physics 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. |
| title | All-Optically Controlled Memristive Reservoir Computing Capable of Bipolar and Parallel Coding |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2602.12938 |