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

•Machine learning (ML) techniques used for resistive switching (RS) devices.•Provide design guidelines using regression tree-based ML techniques.•Performance of RS devices predicted using LM, ANN, and RF algorithms.•Feature importance ranking is determined for RS devices.•ML predictions are validated by fabricating the RS 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. The machine learning (ML) techniques help to accelerate device optimization and provide new insights from the dataset/literature. In this work, ML techniques are utilized to get design guidelines and performance prediction of the CMOS-compatible resistive switching (RS) memory devices based on HfO2/x, Ta2O5, and TaOx materials. The ML predictions are validated by fabricating the RS devices. [Display omitted]