Abstract: A method of fabricating a semiconductor device package includes: providing a die carrier; disposing at least one semiconductor die on the die carrier, the semiconductor die comprising at least one contact pad on a main face remote from the carrier; electrically connecting the semiconductor die or another electrical device with an electrical connector; applying an encapsulant above the semiconductor die, the die carrier, and the electrical connector; and screwing a metallic drilling screw through the encapsulant so that an end of the drilling screw contacts the electrical connector.

Abstract: The semiconductor device package comprises a die carrier, at least one semiconductor die disposed on the carrier, the semiconductor die comprising at least one contact pad on a main face remote from the carrier, an encapsulant disposed above the semiconductor die, an electrical connector electrically connected with the contact pad, a drilling screw screwed through the encapsulant and connected with the electrical connector.

Abstract: A turbine diagnostic machine learning system builds one or more turbine engine performance models using one or more parameter or parameter characteristics. A model of turbine engine performance includes ranked parameters or parameter characteristics, the ranking of which is calculated by a model builder based upon a function of AIC, AUC and p-value, resulting in a corresponding importance rank. These raw parameters and raw parameter characteristics are then sorted according to their importance rank, and selected by a selection component to form one or more completed models. The one or more models are operatively coupled to one or more other models to facilitate further machine learning capabilities by the system.

Abstract: A turbine diagnostic machine learning system builds one or more turbine engine performance models using one or more parameter or parameter characteristics. A model of turbine engine performance includes ranked parameters or parameter characteristics, the ranking of which is calculated by a model builder based upon a function of AIC, AUC and p-value, resulting in a corresponding importance rank. These raw parameters and raw parameter characteristics are then sorted according to their importance rank, and selected by a selection component to form one or more completed models. The one or more models are operatively coupled to one or more other models to facilitate further machine learning capabilities by the system.