Correlation of natural honey-based RRAM processing and switching properties by experimental study and machine learning. (November 2022)

• Record Type: Journal Article
• Authors: Sueoka, Brandon; Yamil Vicenciodelmoral, Abdi; Mehedi Hasan Tanim, Md; Zhao, Xinghui; Zhao, Feng
• Is Part Of: Solid-state electronics. Volume 197(2022)
• Publication Date: 2022-11
• Subjects: Honey -- Nonvolatile memory -- Artificial synaptic device -- Neuromorphic systems -- Resistive switching memory -- Machine learning; Semiconductors -- Periodicals; Semiconducteurs -- Périodiques; 621.38152
• DOI: 10.1016/j.sse.2022.108463
• Languages: English
• ISSNs: 0038-1101

Abstract

Highlights: Natural honey-based RRAM devices were fabricated by different process conditions. Resistive switching properties were tested and correlated with process conditions. SET and RESET voltages were used as dataset to train machine learning algorithms. Four machine learning models obtained 90% accuracy to predict SET voltages. Abstract: 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.