Natural honey is a promising material for hardware components of nonvolatile memory and artificial synaptic devices in emerging renewable and biodegradable neuromorphic systems. The resistive switching properties of these devices are closely correlated with device process conditions. In this paper, honey based resistive random access memory (RRAM) devices were fabricated with different metal electrodes and drying temperature and duration. SET and RESET voltages were measured and used as dataset to train machine learning algorithms. Four machine learning models were applied to process data and demonstrated an average accuracy of 89.9 % to 91.6 % to predict the SET voltages in the range of [0 V, 6 V]. This study established a useful practice for fabrication of RRAM devices based on honey and can be extended to other natural organic materials.

天然蜂蜜是新兴的可再生和可生物降解神经形态系统中非易失性存储器和人工突触设备的硬件组件的有前途的材料。这些器件的阻变特性与器件工艺条件密切相关。在本文中，基于蜂蜜的电阻随机存取存储器 (RRAM) 器件是用不同的金属电极和干燥温度和持续时间制造的。测量 SET 和 RESET 电压并将其用作数据集来训练机器学习算法。四个机器学习模型被应用于处理数据，并展示了 89.9% 到 91.6% 的平均准确度来预测 [0 V, 6 V] 范围内的 SET 电压。这项研究为基于蜂蜜的 RRAM 器件的制造建立了一种有用的实践，并且可以扩展到其他天然有机材料。