One well-established method for simulating the thermal behavior of power switches in motor drive applications is the use of a Cauer network. This method allows the thermal impedance (Zth) curve, an important factor in the selection and design of power switches, to be simulated. This helps determine if the power switch will lead to a working application. The product definition (PD) phase establishes key thermal performance parameters, such as on-state-resistance and Zth, supporting the design phase. Product definition space (PDS) shapes the boundaries of the solution space for the power switch. In this paper, we propose a machine learning (ML) approach to create fast surrogate models as a direct correlation between the Zth curve and the application requirements. This method allows for optimization at a rate that is six orders of magnitude faster, while maintaining a maximum error of just 2%. This enables the experts to create the PDS by a factor of ten thousand faster and could significantly improve and accelerate the design process.     «

One well-established method for simulating the thermal behavior of power switches in motor drive applications is the use of a Cauer network. This method allows the thermal impedance (Zth) curve, an important factor in the selection and design of power switches, to be simulated. This helps determine if the power switch will lead to a working application. The product definition (PD) phase establishes key thermal performance parameters, such as on-state-resistance and Zth, supporting the design pha...     »