  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.
