A Machine Learning-Assisted Model for GaN Ohmic Contacts Regarding the Fabrication Processes

Gallium nitride (GaN) devices have been successfully commercialized due to their superior performance, especially their high-power transformation efficiency. To further reduce the power consumption of these devices, the optimization for the ohmic contacts is attracting more and more attention. In the light of the mature and powerful machine learning (ML) techniques, this work provides a novel method to evaluate the fabrication processes of the ohmic contacts in AlGaN/GaN heterojunction, n-type, and p-type GaN, by establishing a regression-based model. The proposed model can not only investigate the influence weight of each process but also predict the contact resistance by inputting the desired recipes. A website (http://ohmic.zeheng.wang/) containing the successfully trained model for the readers' interests is also provided, which, we believe, would benefit the society of the process development and optimization.

Automated summary

This work utilizes machine learning techniques to establish a regression-based model for evaluating the fabrication processes of ohmic contacts in AlGaN/GaN heterojunction, n-type, and p-type GaN. The model can predict contact resistance and investigate the influence of each process step, with a website provided for readers to explore further.