Electrochemical reservoir computing based on surface-functionalized carbon nanotubes

Abstract

Reservoir computing, a type of neural network computing, is an energy-efficient and fast machine learning method, expected to realize low-power edge processing in sensor devices. This study constructs an electrochemical reservoir using carbon nanotube (CNT) thin-film electrodes on flexible plastic and proves the capability of the electrochemical functionalization of CNT thin films to improve the reservoir's performance. The performance of the time-series data prediction task, called the nonlinear autoregressive moving average (NARMA) task, can be improved using multiple CNT electrodes functionalized under different conditions. Based on the correlation coefficient analysis of the reservoir output, we illustrate that the reservoir dimensionality is enhanced by combining CNT electrodes with different functionalization conditions. Furthermore, the dimensionality and memory capacity of the reservoir can be improved using the displacement current of the electric double layer and the redox current in the electrochemical electrode. We demonstrate the time-series data prediction of blood glucose levels in patients with type 1 diabetes using a physical reservoir for the first time. The use of functionalized CNTs enables flexible and high-performance reservoir computing, leading to the low-power in-sensor edge processing of wearable sensors.