Nano-ionic Solid Electrolyte FET-Based Reservoir Computing for Efficient Temporal Data Classification and Forecasting.

Physical dynamic reservoirs are well-suited for edge systems, as they can efficiently process temporal input at a low training cost by utilizing the short-term memory of the device for in-memory computation. However, the short-term memory of two-terminal memristor-based reservoirs limits the duration of the temporal inputs, resulting in more reservoir outputs per sample for classification. Additionally, forecasting requires multiple devices (20-25) for the prediction of a single time step, and long-term forecasting requires the reintroduction of forecasted data as new input, increasing system complexity and costs. Here, we report an efficient reservoir computing system based on a three-terminal nano-ionic solid electrolyte FET (SE-FET), whose drain current can be regulated via gate and drain voltages to extend the short-term memory, thereby increasing the duration and length of the temporal input. Moreover, the use of a separate control terminal for read and write operation simplifies the design, enhancing reservoir efficiency compared to that in two-terminal devices. Using this approach, we demonstrate a longer mask length or bit sequence, which gives an accuracy of 95.41% for the classification of handwritten digits. Furthermore, this accuracy is achieved using 51% fewer reservoir outputs per image sample, which significantly reduces the hardware and training cost without sacrificing the accuracy of classification. We also demonstrate long-term forecasting by using 50 previous data steps generated by an SE-FET-based reservoir consisting of four devices to predict the next 50 time steps without any feedback loop. This approach results in a low root-mean-square error of 0.06 in the task of chaotic time-series forecasting, which outperforms the standard linear regression machine learning algorithm by 53%.