Two-Dimensional Oscillatory Neural Network Based on Charge-Density-Wave Devices Operating at Room Temperature
Alexander Khitun, Guanxiong Liu, and Alexander A. Balandin

TL;DR
This paper introduces a room-temperature oscillatory neural network using 2D tantalum disulfide devices, demonstrating complex data processing capabilities with potential for scalable, high-frequency applications.
Contribution
It presents a novel neural network architecture based on charge-density-wave 2D materials, with detailed simulation of its dynamics and potential advantages over existing technologies.
Findings
Formation of artificial vortexes in the network
Cellular automata-like data processing observed
High operational frequency and scalability demonstrated
Abstract
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…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Advanced Memory and Neural Computing · Nonlinear Dynamics and Pattern Formation
