Reconfigurable Selector-Free All-Optical Controlled Neuromorphic Memristor for In-Memory Sensing and Reservoir Computing

Lu Chen, Jialin Meng, Jieru Song, Kangli Xu, Tianyu Wang, Hao Zhu, Qingqing Sun, David Wei Zhang, Lin Chen

ACS Nano · 2024 · 25 citations · 34 references

Concepts

TL;DR

The growing demand for data‑driven applications has spurred convergence of image sensing, memory, and computing, yet integrating the full signal‑processing chain into a single device remains challenging. The device’s conductance is optically tuned, allowing it to act as a sensor, memory, and processor, while electrical stimuli trigger integrate‑and‑fire behavior and a reservoir‑computing architecture emulates neuron and synapse functions. The selector‑free, all‑optical memristor demonstrates high rectification and nonlinearity, enabling a low‑energy, low‑latency, hardware‑simplified computing architecture.

Abstract

Recently, the rising demand for data-based applications has driven the convergence of image sensing, memory, and computing unit interfaces. While specialized electronic hardware has spurred advancements in the in-memory and in-sensor computing, integrating the entire signal-processing chain into a single device still faces significant challenges. Here, a reconfigurable all-optical controlled memristor with the selector-free feature is demonstrated. The conductance of the device can be controlled within the pure light domain, which enables it to integrate sensing, memory, and computing together. The integrate-and-fire behavior is also realized through electrical stimuli. Furthermore, the device exhibits an excellent rectifying ratio and nonlinearity to overcome the sneak current. Finally, an in-memory sensing and computing architecture is realized through reservoir computing based on neuron and synaptic functions mimicked by the proposed device. Such an all-in-one paradigm facilitates the computing architecture with low energy consumption, low latency, and reduced hardware complexity.

References

34