Strain-Insensitive, Air-Stable Stretchable Carbon Nanotube-Based Synaptic Transistors Array via Direct Microfabrication for Neuromorphic Computing
Dingzhou Cui, Zhiyuan Zhao, Fugu Tian, Wenbo Chen, Mingrui Chen, Max Zhou, Xiaoqi Wu, Jingxin Zhang, Chongwu Zhou
Abstract
Stretchable synaptic transistors show great promise in mimicking brain activities in soft robotics and skin electronics applications. However, the fabrication of such device arrays on elastic substrates with high stability, throughput, and yield remains challenging. Here, we have developed an approach to fabricate stretchable synaptic transistors directly on elastic substrates, in which carbon nanotubes and SU-8 are used as channel and dielectric, respectively. This method employs a fully photolithography-based microfabrication process that operates at relatively low temperatures. The devices exhibit an average on–off ratio of 2 × 10 6 and show minimal degradation when stretched up to 40%. Single-pulse, paired-pulse, and repetitive-pulse responses are also demonstrated, showing their ability to work as artificial synapses. The devices exhibit a high linearity of ≤1 with 100 distinct conductance states in long-term plasticity and a dynamic range of 15. Furthermore, we conducted a handwritten digit recognition simulation, achieving a learning accuracy of over 90%. We believe our work can serve as a guide for developing high-performance stretchable synaptic devices for various applications.