Physical echo state network based on the nonlinearity and dynamic response of ambipolar heterostructure transistors

In the field of neuromorphic computing, time-series prediction poses a significant challenge to recurrent neural network architectures, often requiring task-specific customization that limits the development of general-purpose computing platforms. In this work, we implement a physical echo-state network (ESN) using ambipolar organic–inorganic heterostructure transistors to form its reservoir layer. Leveraging the ambipolar nature of the transistor, its variable-resistance region enables sparse matrix operations, while the saturation region provides tanh-like nonlinearity, making it well-suited for implementing both synaptic weighting and neuronal activation in an ESN. Additionally, its dynamic response naturally introduces temporal attributes. Thus, it can serve as a neuromorphic computing model for time-series tasks. Without the involvement of dynamic mechanisms, it is capable of performing image recognition, time-series prediction, and multimodal recognition tasks. When dynamic mechanisms are incorporated, the model achieves an accuracy of 96.98% on the MNIST handwritten digit dataset and 86.67% on the Fashion-MNIST dataset. This work offers a neuromorphic computing architecture, providing insights for tasks such as nonlinear mapping and time-series prediction. In neuromorphic computing, time-series prediction is a significant challenge to recurrent neural network architecture. Here, the authors present ambipolar transistors implementing a physical echo-state network for efficient time-series and image tasks, achieving high accuracy via static and dynamic computing modes.