Hybrid optoelectronic encoding in ZnO thin film transistors for multimodal physical reservoirs

Abstract Physical reservoir computing has emerged as an energy-efficient neuromorphic paradigm for processing complex temporal data. However, conventional single-modality reservoirs often suffer from limited internal state richness, constraining their capability to handle high-dimensional tasks. Here, we report a multimodal optoelectronic reservoir computing system based on ZnO thin-film transistors. By exploiting the synergistic interaction between oxygen ion kinetics in the HfO 2 gate dielectric and the persistent photoconductivity effect in the ZnO channel, we identify a robust asymmetric optoelectronic coupling mechanism. Specifically, the electrical gate bias nonlinearly modulates the optical relaxation dynamics, effectively acting as a tunable kernel to expand the reservoir’s feature mapping space. Leveraging this property, we implement a hybrid optoelectronic encoding strategy that achieves a peak recognition accuracy of 90.31% on the MNIST handwritten digit dataset, significantly outperforming single-modality benchmarks. Our analysis reveals that the asymmetric cross-coupling breaks the monotonicity of the device response, thereby enhancing state separability. This work provides a fundamental physical framework for leveraging single-device multimodal dynamics to develop high-performance neuromorphic hardware.