Temporally Reconfigurable Reservoir Computing with Flexible Electrolyte‐Gated TFTs for High‐Performance Neuromorphic Processing

Abstract

Abstract Reservoir computing (RC), a brain‐inspired neuromorphic algorithm, offers simplicity and efficiency for processing spatiotemporal signals. However, conventional RC systems face limitations in handling diverse temporal scales and spatial complexities due to invariant temporal dynamics. This study introduces a temporally reconfigurable RC system utilizing ultrathin, flexible, all‐solid‐state electrolyte‐gated thin‐film transistors (UFLEX TFTs) with high performance: an on/off ratio of ≈10 7 , endurance beyond 2.5 × 10 4 pulses, and low variability. UFLEX TFTs, based on molybdenum disulfide (MoS 2 ) channels and organic–inorganic hybrid AlO x dielectrics, enable modulation of temporal dynamics via simple electrical signals. The system maintains mechanical flexibility and robust performance after bending tests. By extracting features across varied temporal and spatial scales, it achieves classification accuracies of 90.3% for CIFAR‐10 object images and 81.8% for NIH chest X‐ray images. This work lays a foundation for flexible neuromorphic hardware systems capable of efficient, high‐performance spatiotemporal signal processing.