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.