Analysis on reservoir activation with the nonlinearity harnessed from solution-processed MoS2 devices
<|reference_start|>Analysis on reservoir activation with the nonlinearity harnessed from solution-processed MoS2 devices: Reservoir computing is a recurrent neural network that has been applied across various domains in machine learning. The implementation of reservoir computing, however, often demands heavy computations for activating the reservoir. Configuring physical reservoir networks and harnessing the nonlinearity from the underlying devices for activation is an emergent solution to address the computational challenge. Herein, we analyze the feasibility of employing the nonlinearity from solution-processed molybdenum disulfide (MoS2) devices for reservoir activation. The devices, fabricated using liquid-phase exfoliated MoS2, exhibit a high-order nonlinearity achieved by Stark modulation of the MoS2 material. We demonstrate that this nonlinearity can be fitted and employed as the activation function to facilitate reservoir computing implementation. Notably, owing to the high-order nonlinearity, the network exhibits long-term synchronization and robust generalization abilities for approximating complex dynamical systems. Given the remarkable reservoir activation capability, coupled with the scalability of the device fabrication, our findings open the possibility for the physical realization of lightweight, efficient reservoir computing for, for instance, signal classification, motion tracking, and pattern recognition of complex time series as well as secure cryptography. As an example, we show the network can be appointed to generate chaotic random numbers for secure data encryption.<|reference_end|>
@article{liu2024analysis,
    title={Analysis on reservoir activation with the nonlinearity harnessed from
  solution-processed MoS2 devices},
    author={Songwei Liu, Yang Liu, Yingyi Wen, Jingfang Pei, Pengyu Liu, Lekai
  Song, Xiaoyue Fan, Wenchen Yang, Danmei Pan, Teng Ma, Yue Lin, Gang Wang,
  Guohua Hu},
    journal={arXiv preprint arXiv:2403.17676},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17676},
    primaryClass={physics.app-ph cs.ET}
}