Neuromorphic computing, a novel computing configuration inspired by the brain, aims to perform calculations based on physical neurons and synapses, attracting significant attention in recent years. Resistive random access memory (RRAM) shows great potential in this field, demonstrating high operation speed, nanoscale scalability, long retention time, non-volatile performance, and a simple structure.

Despite the promising performance of RRAM, a high forming voltage potentially hinders the widespread application of the device. This thesis aims to diminish and eliminate the forming voltage. To achieve this, different metals were inserted between the insulator layer and the bottom electrode of the RRAM, serving as an interface metal layer. The interface metal was expected to introduce oxygen vacancies to the insulator, thereby decreasing the forming voltage. Advanced nanofabrication processes were employed in the cleanroom, and a related recipe was developed. The influence of layer thickness and device area was also studied to gain a comprehensive understanding. Among all the samples, Ru-based devices were observed to be forming-free.

Data analysis methods were applied to model the data, with the random forest method found to be the most suitable, achieving an accuracy of 82.4%. The model was verified by measurements of 10 nm Ru-based devices. Feature importance was then calculated to interpret the model. The four most important features determining the forming voltage are the thickness, standard electrode potential, area, and work function of the interface metal. This work adopts a new approach to eliminating the forming voltage, not only providing a forming-free device but also offering a guideline for future research on forming voltage.