Abstract: Methods, systems, and computer programs are presented for determining the recipe for manufacturing a semiconductor with the use of machine learning (ML) to accelerate the definition of recipes. One general aspect includes a method that includes an operation for performing experiments for processing a component, each experiment controlled by a recipe, from a set of recipes, that identifies parameters for manufacturing equipment. The method further includes an operation for performing virtual simulations for processing the component, each simulation controlled by one recipe from the set of recipes. An ML model is obtained by training an ML algorithm using experiment results and virtual results from the virtual simulations. The method further includes operations for receiving specifications for a desired processing of the component, and creating, by the ML model, a new recipe for processing the component based on the specifications.

Abstract: A method and product produced includes a power layer board that is manufactured by providing a printed circuit board having an input configured to receive an input power and an output configured to deliver an output power conditioned to power a motor. The method also includes soldering a first component to the printed circuit board. Thereafter, a request to manufacture the power layer board is received that includes parameters of the input power and/or the output power. Therefrom, characteristics of a second component including a resistor, a capacitor, and/or an inductor is identified based on the parameters of the input power and/or the output power. Accordingly, the identified second component is mounted to the printed circuit board through a solder-less cold-weld connector to complete the power layer board.