Unraveling the importance of fabrication parameters of copper oxide-based resistive switching memory devices by machine learning techniques.

ABSTRACT: In the present study, various statistical and machine learning (ML) techniques were used to understand how device fabrication parameters affect the performance of copper oxide-based resistive switching (RS) devices. In the present case, the data was collected from copper oxide RS devices-based research articles, published between 2008 to 2022. Initially, different patterns present in the data were analyzed by statistical techniques. Then, the classification and regression tree algorithm (CART) and decision tree (DT) ML algorithms were implemented to get the device fabrication guidelines for the continuous and categorical features of copper oxide-based RS devices, respectively. In the next step, the random forest algorithm was found to be suitable for the prediction of continuous-type featu

SUBMITTER: Patil SM 

PROVIDER: S-EPMC10039863 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature