Two-Dimensional Oscillatory Neural Network Based on Room-Temperature Charge-Density-Wave Devices

We propose an oscillatory neural network implemented with two-dimensional (2-D) tantalum disulfide devices operating in the change density wave regime at room temperature. An elementary cell of the network consists of two 1T-TaS
₂
devices connected in series. Such a cell has a constant output and oscillatory states. All cells have the same bias voltage. There is a constant current flowing through the cell in the constant output mode. The oscillations occur at a certain bias voltage due to the electrical-field triggered metal-to-insulator transition owing to the changes in the charge-density-wave phase in the 1T-TaS
₂
channel. Two 1T-TaS
₂
devices oscillate out-of-phase when 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 30 × 30 node network. We present the examples of pattern recognition on a template with the fixed coupling among the near-neighbor cells. We also present the results of our numerical modeling mimicking the Game of Life in the network with the time-varying coupling. The 2-D 1T-TaS
₂
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.