Modern computing applications increasingly rely on technologies in artificial intelligence, machine learning, and big data analytics. These applications often demand more powerful and energy-efficient hardware. Resistive switching random access memory (ReRAM) has emerged as a promising solution to satisfy both the storage and computing needs. In this paper, a natural organic honey film embedded with carbon nanotube (CNT) was fabricated into a resistive switching device, and the resistive switching behaviors were investigated. Endurance test results show the cycle-to-cycle variation of set and reset voltages. On/Off ratio in retention test was found to be in the order of ~105which proves its potential as a non-volatile memory device to support neuromorphic computing applications. This research opens up opportunities to execute big data and machine learning applications with modest energy consumption and minimal electronic waste.

The growing popularity of big data and machine learning applications call for a more powerful and energy-efficient way to execute deep learning workflows. Neuromorphic chips provide a potential solution, as they attempt to mimic the neuronal architectures in human brain and show great potentials in reducing energy consumption in the order of magnitude and also improve the computational performance. However, the fabrication process for neuromophic chips is costly and currently based on trial-and-error, which adds complexity to the design process. In this paper, we address this challenge by designing and developing machine learning guided microfabrication process for Resistive Random Access Memory (RRAM), which is a key device in neuromorphic chips. Experimental results show that our approach is effective in terms of predicting the performance of RRAM devices fabricated under various process conditions.