A stacked regression–based TCAD–ML framework for accelerated GaN transistor design

Key Points

• Accelerated design through a TCAD–ML framework enhances gallium nitride transistor performance and efficiency.
• The framework integrates machine learning with stacked regression, yielding improved design outcomes.
• Comprehensive analysis of gallium nitride transistors highlights novel methodologies for optimization.
• This approach may enable faster development of efficient transistors, supporting advanced electronic applications.