Designing power electronic switches in a timely manner is essential for a wide range of electrical applications. The challenge arises when determining the acceptable design parameters from the product definition space that lead to a functioning application. Creating a well-defined product definition space can help reduce design cycle time and minimize the risk of design failure or non-compliance with requirements. In response to this, we propose a machine learning-based framework to create this space with substantially less simulations in comparison to exhaustive and optimization-based methods. This is particularly beneficial in higher dimensions where running numerous simulations may not be feasible. We applied this approach to generic silicon power switches within a half-bridge motor drive application simulation, defining borders that closely match-above 98%-to the borders of the product definition space. In this use case, the need for simulations is reduced by a factor of five, while still meeting all operating conditions and requirements.    «

Designing power electronic switches in a timely manner is essential for a wide range of electrical applications. The challenge arises when determining the acceptable design parameters from the product definition space that lead to a functioning application. Creating a well-defined product definition space can help reduce design cycle time and minimize the risk of design failure or non-compliance with requirements. In response to this, we propose a machine learning-based framework to create this...    »