The flexible electronics has been deemed to be a promising approach to the wearable electronic systems. However, the mismatching between the existing flexible deices and the conventional computing paradigm results an impasse in this field. In this work, a new way to access to this goal is proposed by combining flexible devices and the neuromorphic architecture together. To achieve that, a high-performance flexible artificial synapse is created based on a carefully designed and optimized memristive transistor. The device exhibits high-performance which has near-linear non-volatile resistance change under 10,000 identical pulse signals within the 515% dynamic range, and has the energy consumption as low as 45 fJ per pulse. It also displays multiple synaptic plasticity features, which demonstrates its potential for real-time online learning. Besides, the adaptability by virtue of its three-terminal structure specifically contributes its improved uniformity, repeatability, and reduced power consumption. This work offers a very viable solution for the future wearable computing.

柔性电子器件被认为是可穿戴电子系统的一种有前途的方法。然而，现有的柔性装置与常规计算范例之间的不匹配导致该领域的僵局。在这项工作中，通过将灵活的设备和神经形态架构结合在一起，提出了一种实现此目标的新方法。为此，基于精心设计和优化的忆阻晶体管，创建了高性能的柔性人工突触。该器件具有高性能，在515％动态范围内的10,000个相同脉冲信号下具有近乎线性的非易失性电阻变化，并且每脉冲的能量消耗低至45 fJ。它还显示了多种突触可塑性特征，从而证明了其在实时在线学习中的潜力。除了，凭借其三端子结构的适应性，特别有助于提高其均匀性，可重复性和降低功耗。这项工作为将来的可穿戴计算提供了一个非常可行的解决方案。