Back to Search
Start Over
Using Graph Attention Network to Reversely Design GaN MIS-HEMTs Based on Hand-Drawn Characteristics
- Source :
- IEEE Access, Vol 11, Pp 70168-70173 (2023)
- Publication Year :
- 2023
- Publisher :
- IEEE, 2023.
-
Abstract
- In this work, the methodology using Graph Attention Network (GAT) for the reserve design in GaN power MIS-HEMTs based on hand-drawn characteristics is demonstrated for the first-time. The hand-drawn ID-VG characteristic is constructed by Ramer-Douglas-Peucker algorithm. Then, the extracted information is sent to the Graph Attention Network to receive the corresponding device design variables, including tAlGaN, recessed depth, Al%, Lg, Lgd, and Lgs. Less than 30 seconds is consumed to generate the design variables and less than 8% of the differences in the key extracted parameters, such as threshold voltage (Vth), On-state current (Ion), and subthreshold slope (SS), can be achieved by comparing hand-drawn ID-VG and simulated ID-VG characteristic based on the design variables from GAT model. Therefore, the developed GAT approach is promising for the reverse design of GaN power MIS-HEMTs, which can provide users with efficient and valuable design suggestions to optimize the devices toward the targeting performance.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.5685da3d39fc4d90a1a3d008a5cb023a
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2023.3293001