Neuromorphic systems are expected to be a breakthrough beyond the conventional von Neumann architecture when implementing an artificial neural network. In a neuromorphic system, analog synaptic devices store the synaptic weight values of an artificial neural network. Among various memory devices, RRAM-based synaptic device has several advantages such as excellent scaling potential with a simple two-terminal structure and low energy consumption during the read and write operations. However, it has an inherent limitation of abrupt and nonlinear change in the conductance characteristics. Here, we investigate the non-ideal characteristics of conductance modulation using a fabricated RRAM device. We also analyze the impact of non-ideal conductance modulation on pattern recognition accuracy through a device-to-system level simulation. In addition, to solve the drawback of the previous conductance update method (occasional RESET), we propose a new conductance update method (occasional RESET without re-write). This comprehensive experiment and device-to-system level study can facilitate the realization of reliable learning performance on RRAM-based neuromorphic systems.

在实现人工神经网络时，神经形态系统有望成为超越传统冯·诺依曼架构的突破。在神经形态系统中，模拟突触设备存储人工神经网络的突触权重值。在各种存储设备中，基于RRAM的突触设备具有多个优势，例如具有出色的缩放潜力，简单的两端子结构以及在读写操作期间的低能耗。然而，其固有的局限性在于电导特性的突然和非线性变化。在这里，我们研究使用制造的RRAM器件进行电导调制的非理想特性。我们还通过设备到系统级的仿真分析了非理想电导调制对模式识别精度的影响。此外，为了解决以前的电导更新方法（偶尔复位）的缺点，我们提出了一种新的电导更新方法（不进行重写的临时RESET）。这项全面的实验和设备到系统级的研究可以促进在基于RRAM的神经形态系统上实现可靠的学习性能。