Liu Riping, He Yifei, Zhu Xiuyuan, Duan Jiayao, Liu Chuan, Xie Zhuang, McCulloch Iain, Yue Wan
Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, 510275, P. R. China.
State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, P. R. China.
Adv Mater. 2025 Jan;37(3):e2409258. doi: 10.1002/adma.202409258. Epub 2024 Nov 22.
Organic electrochemical synaptic transistors (OESTs), inspired by the biological nervous system, have garnered increasing attention due to their multifunctional applications in neuromorphic computing. However, the practical implementation of OESTs for signal recognition-particularly those utilizing n-type organic mixed ionic-electronic conductors (OMIECs)-still faces significant challenges at the hardware level. Here, a state-of-the-art small-molecule n-type OEST integrated within a physically simple and hardware feasible reservoir-computing (RC) framework for practical temporal signal recognition is presented. This integration is achieved by leveraging the adjustable synaptic properties of the n-OEST, which exhibits tunable nonlinear short-term memory, transitioning from volatility to nonvolatility, and demonstrating adaptive temporal specificity. Additionally, the nonvolatile OEST offers 256 conductance levels and a wide dynamic range (≈147) in long-term potentiation/depression (LTP/LTD), surpassing previously reported n-OESTs. By combining volatile n-OESTs as reservoirs with a single-layer perceptron readout composed of nonvolatile n-OEST networks, this physical RC system achieves substantial recognition accuracy for both handwritten-digit images (94.9%) and spoken digit (90.7%), along with ultrahigh weight efficiency. Furthermore, this system demonstrates outstanding accuracy (98.0%) by grouped RC in practical sleep monitoring, specifically in snoring recognition. Here, a reliable pathway for OMIEC-driven computing is presented to advance bioinspired hardware-based neuromorphic computing in the physical world.
受生物神经系统启发的有机电化学突触晶体管（OESTs），因其在神经形态计算中的多功能应用而受到越来越多的关注。然而，OESTs在信号识别方面的实际应用——特别是那些利用n型有机混合离子电子导体（OMIECs）的应用——在硬件层面仍面临重大挑战。在此，本文展示了一种集成在物理上简单且硬件可行的储层计算（RC）框架内的先进小分子n型OEST，用于实际的时间信号识别。这种集成是通过利用n - OEST可调的突触特性实现的，该特性表现出可调的非线性短期记忆，从易失性转变为非易失性，并展示出自适应时间特异性。此外，非易失性OEST在长时程增强/抑制（LTP/LTD）中提供256个电导水平和宽动态范围（≈147），超过了先前报道的n - OESTs。通过将易失性n - OESTs作为储层与由非易失性n - OEST网络组成的单层感知器读出相结合，这种物理RC系统在手写数字图像（94.9%）和语音数字（90.7%）识别方面都取得了较高的准确率，同时具有超高的权重效率。此外，该系统在实际睡眠监测中，特别是在打鼾识别中，通过分组RC展示了出色的准确率（98.0%）。在此，本文提出了一条由OMIEC驱动的计算可靠途径，以推动物理世界中基于生物启发硬件的神经形态计算发展。
