MXene‐Based Broadband Ultrafast Nonlinear Activator for Optical Computing
Yang Zhan, Weimin Tan, Tianju Zhang, Chenduan Chen, Zixin Wang, Yu Mao, Chenxi Ma, Qing Lin, Wanjun Bi, Fei Yu, Bo Yan, Jun Wang
Abstract
Abstract Optical neural networks (ONNs) are particularly advantageous owing to their inherent parallelism and low energy consumption. However, one of the obstacles to the implementation of ONNs is the lack of optical nonlinearity. In this study, optical nonlinear activators for ONNs are prepared by combining Ti 3 C 2 T x MXene with microfibers and their principles are verified. Activation functions obtained from experimental measurements are used to simulate multiclassification and super‐resolution reconstruction tasks with performance comparable to that of activation functions commonly used in computers. Four necessary criteria are proposed and validated for evaluating the performance of the nonlinear activator: recovery time, deviation from linearity, the activation function close to identity mapping, and reconfigurability of the configuration. Theoretically, the nonlinear activator can compute 100 times faster than commonly used electronic computers and can be used as a nonlinear activation unit for ONNs to help the integration of ONNs with artificial intelligence.