Disposable and Flexible Paper-Based Optoelectronic Synaptic Devices for Physical Reservoir Computing

Abstract

Health monitoring using wearable artificial intelligence (AI) sensors with sensing and cognitive capabilities has garnered significant attention. The development of self-contained AI sensors that can operate with low power consumption, akin to the human brain, is necessary. Physical reservoir computing (PRC), which mimics the human brain using physical phenomena, offers a low-power consumption architecture. Nevertheless, creating a flexible and easily disposable sensors using PRC capable of processing optical signals with sub-second response times suitable for biological signals presents a challenge. In this study, a disposable and flexible paper-based optoelectronic synaptic devices are designed, which are composed of nanocellulose and ZnO nanoparticles, for PRC. This device exhibits synaptic photocurrent in response to optical input. To assess its performance, a classification and time-series forecasting tasks are conducted. The memory capacity of short-term memory task, indicating the device's ability to store past information, is 1.8. The device can recognize handwritten digits with an accuracy of 88%. These results highlight the potential of the device for PRC. In addition, subjecting the device to 1000 rounds of bending do not affect its accuracy. Furthermore, the device burn in a few seconds, much like regular office paper, demonstrating its disposability.