Machine learning-assisted design guidelines and performance prediction of CMOS-compatible metal oxide-based resistive switching memory devices

Machine learning (ML) has accelerated the discovery of new materials and properties of electronic devices, reducing development time and increasing efficiency. In this study, ML was used to provide design guidelines and predict the performance of industry-standard resistive switching (RS) memory devices based on HfO2/x, Ta2O5, and TaOx materials. The model building, analyses, and prediction processes were based on a database of peer-reviewed articles published between 2007 and 2020. More than 15,000 property entries were used for our ML tasks. Moreover, supervised and unsupervised ML techniques were used to provide design guidelines for the categorical and continuous feature sets. In addition, a linear model, artificial neural network, and the random forest algorithm were employed to predict the continuous-type features, and gradient boosting was used to understand how device parameters can affect RS performance. Finally, the ML predictions were validated by fabricating the corresponding RS devices. The results indicated that the ML techniques accelerated the discovery and understanding of different RS properties.

Automated summary

Machine learning has been used to accelerate the discovery of new materials and properties of electronic devices. In this study, it was applied to provide design guidelines and predict the performance of industry-standard resistive switching memory devices. The analysis was based on a database of peer-reviewed articles, and supervised and unsupervised ML techniques were utilized. The predictions were validated through the fabrication of corresponding devices.