Abstract: Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.

Abstract: Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.

Abstract: A framework for an iterative reconstruction algorithm is described which combines two or more of an ordered subset method, a preconditioner method, and a nested loop method. In one type of implementation a nested loop (NL) structure is employed where the inner loop sub-problems are solved using ordered subset (OS) methods. The inner loop may be solved using OS and a preconditioner method. In other implementations, the inner loop problems are created by augmented Lagrangian methods and then solved using OS method.

Abstract: Methods are proposed for object positioning for an X-Y projected capacitive touch panel. In an embodiment, capacitance sensing under inphase excitation of traces is applied to set a base value when the capacitive touch panel is not touched and to obtain capacitances at intersections when the capacitive touch panel is touched, and the base value is compared with the measured capacitances to identify touch points. In other embodiments, X-Y projected sensing and all-point sensing are combined to reduce the amount of calculation and achieve the same positioning effect as an all-point capacitive touch panel.

Abstract: A framework for an iterative reconstruction algorithm is described which combines two or more of an ordered subset method, a preconditioner method, and a nested loop method. In one type of implementation a nested loop (NL) structure is employed where the inner loop sub-problems are solved using ordered subset (OS) methods. The inner loop may be solved using OS and a preconditioner method. In other implementations, the inner loop problems are created by augmented Lagrangian methods and then solved using OS method.

Abstract: A capacitive touch panel has a plurality of traces including a boundary trace, and a virtual trace is defined outward of the boundary trace and assigned with a virtual coordinate and a virtual capacitance for interpolation to position a touch point around the boundary of the capacitive touch panel, thereby eliminating the non-addressable region of the capacitive touch panel.

Abstract: Methods are proposed for object positioning for an X-Y projected capacitive touch panel. In an embodiment, capacitance sensing under inphase excitation of traces is applied to set a base value when the capacitive touch panel is not touched and to obtain capacitances at intersections when the capacitive touch panel is touched, and the base value is compared with the measured capacitances to identify touch points. In other embodiments, X-Y projected sensing and all-point sensing are combined to reduce the amount of calculation and achieve the same positioning effect as an all-point capacitive touch panel.

Abstract: A capacitive touch panel has a plurality of traces including a boundary trace, and a virtual trace is defined outward of the boundary trace and assigned with a virtual coordinate and a virtual capacitance for interpolation to position a touch point around the boundary of the capacitive touch panel, thereby eliminating the non-addressable region of the capacitive touch panel.