Regression Model for the Specific Contact Resistance of SiC Ohmic Contacts
Author(s)
Nicholls, Jordan R
Dimitrijev, Sima
Griffith University Author(s)
Year published
2021
Metadata
Show full item recordAbstract
The number of variables involved in the formation of Ohmic contacts to SiC is large, and their relationships to the final contact resistance are often unclear. As such, trial-and-error methods are typically employed to develop or improve SiC contacts. In pursuit of a better alternative, we developed and tested several regression models to predict the specific contact resistance of Ni, Ti, and Al based contacts on both n- and p-type SiC. Literature data was used to train linear regression, Gaussian process regression, and neural network (NN) ensemble models; of these, the NN ensemble was the most effective at predicting contact ...
View more >The number of variables involved in the formation of Ohmic contacts to SiC is large, and their relationships to the final contact resistance are often unclear. As such, trial-and-error methods are typically employed to develop or improve SiC contacts. In pursuit of a better alternative, we developed and tested several regression models to predict the specific contact resistance of Ni, Ti, and Al based contacts on both n- and p-type SiC. Literature data was used to train linear regression, Gaussian process regression, and neural network (NN) ensemble models; of these, the NN ensemble was the most effective at predicting contact resistances. We then applied the model to optimize the annealing schedule for Ni contacts to n-type 4H-SiC, and Ti/Al contacts to p-type 4H-SiC. Finally, we use the model to generate optimal simultaneous contact recipes.
View less >
View more >The number of variables involved in the formation of Ohmic contacts to SiC is large, and their relationships to the final contact resistance are often unclear. As such, trial-and-error methods are typically employed to develop or improve SiC contacts. In pursuit of a better alternative, we developed and tested several regression models to predict the specific contact resistance of Ni, Ti, and Al based contacts on both n- and p-type SiC. Literature data was used to train linear regression, Gaussian process regression, and neural network (NN) ensemble models; of these, the NN ensemble was the most effective at predicting contact resistances. We then applied the model to optimize the annealing schedule for Ni contacts to n-type 4H-SiC, and Ti/Al contacts to p-type 4H-SiC. Finally, we use the model to generate optimal simultaneous contact recipes.
View less >
Journal Title
IEEE Transactions on Semiconductor Manufacturing
Volume
34
Issue
4
Subject
Electrical engineering