Abstract: A parameter candidate for a semiconductor element is provided. A data set of measurement data is provided to a parameter extraction portion, and a model parameter is extracted. A first netlist is provided to a circuit simulator, simulation is performed using the first netlist and the model parameter, and a first output result is output. A classification model learns the model parameter and the first output result and classifies the model parameter. A second netlist and a model parameter are provided to the circuit simulator. A variable to be adjusted is supplied to a neural network, an action value function is output, and the variable is updated. The circuit simulator performs simulation using the second netlist and the model parameter. When a second output result to be output does not satisfy conditions, a weight coefficient of the neural network is updated. When the second output result satisfies the conditions, the variable is judged to be the best candidate.

Abstract: Disclosed are cosmetic treatment articles comprising: (1) a water-insoluble substrate; (2) a gel composition comprising: (a) a first gel forming composition comprising a water soluble polymeric gelling agent and (b) a second gel forming composition comprising a gel strengthening agent; and (3) preferably, a treatment composition comprising a rheology modifier. Further disclosed are cosmetic treatment articles comprising at least two different treatment compositions, each treatment composition being provided at one or more selected locations on the water insoluble substrate.