Abstract: A method for calculating decision variables is configured to calculate the unconfirmed decision variables. First, the method provides a trained predictive mode obtained by machine learning through a machine learning method on a dataset. Next, the method transforms the objective function of the trained predictive model from a constrained objective function to an unconstrained objective function. The method then solves the optimization problem of the unconstrained objective function, wherein the optimizer, trained with the trained predictive model, calculates gradients to facilitate the solution process. Additionally, the samples from the dataset used to train the trained predictive model can be utilized to determine the initial samples for solving the optimization problem. The method for calculating decision variables of the invention can also add a dummy layer in front of the trained predictive model.

Abstract: A decision variable calculation method allowing a of reverse derivation requester to calculate decision variables based on a target result by pre-trained models provided by some participants in federated learning. The method includes the steps of providing the target result to each pre-trained model participating in this method and allowing them to reversely derive the input parameters, forming a loss function based on the difference between each pre-trained model and target result, integrating all input parameters into a total input parameter, and integrating all loss functions into a total loss function. An optimization problem is then constructed by the total input parameters and the total loss function. The solution to the optimization problem is the required decision variables.

Abstract: The present invention provides a method for calculating decision variables. A dummy layer is added at an input layer of a trained neural network predictive model. The dummy layer includes a plurality of artificial neurons respectively connected to a corresponding input terminal of the input layer for the trained predictive model by a newly established link. The input value of each artificial neuron is set to 1, the bias value of the activation function is set to 0, and the output of the activation function is set to 1 when the input of the activation function is 1. The initial weight value of the newly established link is selected and set, and the weight values can be considered as decision variables, wherein the weight values can have ranges or other inter-conditional restrictions.

Abstract: A method for calculating feasible process parameters to achieve a given process result and the method comprises the following steps of: providing a trained prediction model, obtained by machine learning of a dataset by a machine learning method, wherein the dataset comprises a plurality of samples, each of the samples comprises a plurality of sample parameters, and the trained predictive model is configured to input a plurality of input parameters and generate a prediction result corresponding to the input parameters; setting an expected result as the prediction result of the trained predictive model and providing at least one confirmed input parameters of the input parameters; and comparing the expected result, the at least one confirmed input parameters, and the sample parameters of the samples in the dataset by a reverse derivation algorithm to determine at least one non-confirmed input parameters of the input parameters.

Abstract: A federated learning contribution calculation method comprises the following steps: a plurality of participants collaboratively developing a federated aggregation model by federated learning method according to their own local datasets; excluding the participation of at least one first participant in all participants, and then the remained participants collaboratively developing a contribution model by federated learning method; and, comparing the value of the first contribution model and the value of the federated model to obtain the contribution of the at least one first participant. The method of the present invention is capable of calculating the contribution(s) of single participant or multiple participants in the federated learning by few additional information and few additional calculations, so as to achieve the fair profit sharing according to the contributions.

Abstract: A method for calculating feasible process parameters of the present invention constructs an optimization model by treating a trained predictive model as a function of process parameters to be determined, wherein the objective function is to minimize the difference between the function value and the target result, subject to the conditions that the process parameters to be determined must satisfy, either individually or in relation to each other. Moreover, in order to solve the constrained optimization problem, the method with penalty function and barrier function can be used to convert the constrained optimization problem into an unconstrained optimization problem to make it more convenient to solve.

Abstract: A leadframe includes a die pad, a plurality of tie bars, a plurality of metal extrusions and a plurality of leads. The leads are arranged around the die pad. The tie bars are connected to the corners of the die pad, and the metal extrusions are connected to the sides of the die pad but separated from the tie bars. Each metal extrusion has a locking hole and a bonding surface, which is higher than the die pad. The metal extrusions are configured for improving ground connections by wire-bonding. When a bottom surface of the die pad is exposed from an encapsulant for a semiconductor package, the metal extrusions help to secure the die pad without stress transmission.

Abstract: A leadframe includes a die pad, a plurality of tie bars, a plurality of metal extrusions and a plurality of leads. The leads are arranged around the die pad. The tie bars are connected to the corners of the die pad, and the metal extrusions are connected to the sides of the die pad but separated from the tie bars. Each metal extrusion has a locking hole and a bonding surface, which is higher than the die pad. The metal extrusions are configured for improving ground connections by wire-bonding. When a bottom surface of the die pad is exposed from an encapsulant for a semiconductor package, the metal extrusions help to secure the die pad without stress transmission.