Mechanical Metamaterials for Handwritten Digits Recognition
Lingling Wu, Yuyang Lu, Penghui Li, Yong Wang, Jiacheng Xue, Xiaoyong Tian, Shenhao Ge, Xiaowen Li, Zirui Zhai, Junqiang Lu, Xiaoli Lu, Dichen Li, Hanqing Jiang
Abstract
The increasing needs for new types of computing lie in the requirements in harsh environments. In this study, the successful development of a non-electrical neural network is presented that functions based on mechanical computing. By overcoming the challenges of low mechanical signal transmission efficiency and intricate layout design methodologies, a mechanical neural network based on bistable kirigami-based mechanical metamaterials have designed. In preliminary tests, the system exhibits high reliability in recognizing handwritten digits and proves operable in low-temperature environments. This work paves the way for a new, alternative computing system with broad applications in areas where electricity is not accessible. By integrating with the traditional electronic computers, the present system lays the foundation for a more diversified form of computing.