Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing
Xuemeng Hu, Jialin Meng, Tianyang Feng, Tianyu Wang, Hao Zhu, Qingqing Sun, David Wei Zhang, Lin Chen
Abstract
Traditional computing structures are blocked by the von Neumann bottleneck, and neuromorphic computing devices inspired by the human brain which integrate storage and computation have received more and more attention. Here, a flexible organic device with 2,7-dioctyl[1] benzothieno [3,2-b][1] benzothiophene (C8-BTBT) and 2,9-didecyldinaphtho [2,3-b:2′,3′-f] thieno [3,2-b] thiophene (C10-DNTT) heterostructural channel having excellent synaptic behaviors was fabricated on muscovite (MICA) substrate, which has a memory window greater than 20 V. This device shows better electrical characteristics than organic field effect transistors with single organic semiconductor channel. Furthermore, the device simulates organism synaptic behaviors successfully, such as paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/LTD) process, and transition from short-term memory (STM) to long-term memory (LTM) by optical and electrical modulations. Importantly, the neuromorphic computing function was verified using the Modified National Institute of Standards and Technology (MNIST) pattern recognition, with a recognition rate nearly 100% without noise. This research proposes a flexible organic heterojunction with the ultra-high recognition rate in MNIST pattern recognition and provides the possibility for future flexible wearable neuromorphic computing devices.