The hardware design of supervised learning (SL) in spiking neural network (SNN) prefers 3-terminal memristive synapses, where the third terminal is used to impose supervise signals. In this work we address this demand by fabricating graphene transistor gated through organic ferroelectrics of polyvinylidene fluoride. Through gate tuning not only is the nonvolatile and continuous change of graphene channel conductance demonstrated, but also the transition between electron-dominated and hole-dominated transport. By exploiting the adjustable bipolar characteristic, the graphene–ferroelectric transistor can be electrically reconfigured as potentiative or depressive synapse and in this way complementary synapses are realized. The complementary synapse and neuron circuit is then constructed to execute remote supervise method (ReSuMe) of SNN, and quick convergence to successful learning is found through network-level simulation when applying to a SL task of classifying 3 × 3-pixel images. The presented design of graphene–ferroelectric transistor-based complementary synapses and quantitative simulation may indicate a potential approach to hardware implementation of SL in SNN.

尖峰神经网络（SNN）中的监督学习（SL）的硬件设计更喜欢3终端忆阻突触，其中第三个终端用于施加监督信号。在这项工作中，我们通过制造通过聚偏二氟乙烯的有机铁电材料选通的石墨烯晶体管来满足这一需求。通过栅极调谐，不仅可以显示出石墨烯沟道电导率的非易失性和连续性变化，而且还可以显示电子为主的传输和空穴为主的传输之间的过渡。通过利用可调节的双极特性，可以将石墨烯-铁电晶体管电重新配置为增强或抑制突触，并以此方式实现互补突触。然后构造互补的突触和神经元电路以执行SNN的远程监督方法（ReSuMe），当应用于将3×3像素图像分类的SL任务时，可以通过网络级仿真找到成功学习的快速收敛点。提出的基于石墨烯-铁电晶体管的互补突触设计和定量仿真可能表明在SNN中SL的硬件实现的潜在方法。