  We propose an oscillatory neural network implemented with two-dimensional
tantalum disulfide devices operating in the change density wave regime at room
temperature. An elementary cell of the network consists of two 1T-TaS2 devices
connected in series. Such a cell has constant output and oscillatory states.
All cells have the same bias voltage. There is constant current flowing through
the cell in the constant output mode. The oscillations occur at a certain bias
voltage due to the electrical-field driven metal-to-insulator transition owing
to the changes in the charge density wave phase in the 1T-TaS2 channel. Two
1T-TaS2 devices oscillate out-of-phase where one of the devices is in the
insulator phase while the other one is in the metallic state. The
nearest-neighbor cells are coupled via graphene transistors. The cells are
resistively coupled if the graphene transistor is in the On state while they
are capacitively coupled if the transistor is in the Off state. The operation
of the oscillatory neural network is simulated numerically for the 30x30 node
network. The results of our numerical modeling show the formation of artificial
vortexes and cellular-automata type data processing. The two-dimensional
1T-TaS2 devices, utilized in the network, offer a unique combination of
properties such as scalability, high operational frequency, fast
synchronization speed, and radiation hardness, which makes them promising for
both consumer electronic and defense applications.
