Reservoir computing has been considered as a promising intelligent computing paradigm for effectively processing complex temporal information. Exploiting tunable and reproducible dynamics in the single electronic device have been desired to implement the reservoir and the readout layer of reservoir computing system. Two-dimensional moire material, with an artificial lattice constant many times larger than the atomic length scale, is one type of most studied artificial quantum materials in community of material science and condensed-matter physics over the past years. These materials are featured with gate-tunable periodic potential and electronic correlation, thus varying the electric field allows the electrons in the moire potential per unit cell to exhibit distinct and reproducible dynamics, showing great promise in robust reservoir computing. Here, we report that a moire synaptic transistor can be used to implement the reservoir computing system with a homogeneous reservoir-readout architecture. The synaptic transistor is fabricated based on a h-BN/bilayer graphene/h-BN moire heterostructure, exhibiting ferroelectricity-like hysteretic gate voltage dependence of resistance. Varying the magnitude of the gate voltage enables the moire transistor to be switched between long-term memory and short-term memory with nonlinear dynamics. By employing the short- and long-term memory as the reservoir nodes and weights of the readout layer, respectively, we construct a full-moire physical neural network and demonstrate that the classification accuracy of 90.8% can be achieved for the MNIST handwritten digit database. Our work would pave the way towards the development of neuromorphic computing based on the moire materials.
