论文标题
基于铁电隧道连接的集成和开火神经元
A Ferroelectric Tunnel Junction-based Integrate-and-Fire Neuron
论文作者
论文摘要
基于事件的神经形态系统通过使用人工神经元和突触以尖峰形式对数据进行处理，从而提供了低功率解决方案。铁电隧道连接（FTJ）是超低功率存储器，非常适合将其集成到这些系统中。在这里，我们提出了一个混合FTJ-CMOS集成和形式神经元，该神经元构成了用于边缘计算的新代神经形态网络的基本构建块。我们通过调整FTJ设备的切换来证明可以实现电气调谐的神经动力学。
Event-based neuromorphic systems provide a low-power solution by using artificial neurons and synapses to process data asynchronously in the form of spikes. Ferroelectric Tunnel Junctions (FTJs) are ultra low-power memory devices and are well-suited to be integrated in these systems. Here, we present a hybrid FTJ-CMOS Integrate-and-Fire neuron which constitutes a fundamental building block for new-generation neuromorphic networks for edge computing. We demonstrate electrically tunable neural dynamics achievable by tuning the switching of the FTJ device.
