摘要:
A processing method includes processing a wafer based on initial data, measuring errors for each of the plurality of areas, calculating an error similarity of at least some of the plurality of areas as a function of a separation distance between each pair of some of the areas, selecting a first area and a plurality of second areas adjacent to the first area, calculating weight values for the second areas based on the error similarities between each pair of second areas and the error similarities between the first area and each second area, calculating an estimated error of the first area based on the measured errors of the second areas and the weight values for the second areas, and generating estimated data based on the estimated errors for each of the plurality of areas.
摘要:
A processing method includes processing a wafer based on initial data, measuring errors for each of the plurality of areas, calculating an error similarity of at least some of the plurality of areas as a function of a separation distance between each pair of some of the areas, selecting a first area and a plurality of second areas adjacent to the first area, calculating weight values for the second areas based on the error similarities between each pair of second areas and the error similarities between the first area and each second area, calculating an estimated error of the first area based on the measured errors of the second areas and the weight values for the second areas, and generating estimated data based on the estimated errors for each of the plurality of areas.
摘要:
In a simulation system and method thereof, the simulation includes, when a function value for a nominal point (NP) of an input is a first value, running a first simulation on the input; and when the function value for the NP of the input is a second value different from the first value, running a second simulation on the input. Here, the running of the second simulation may include (a) setting a boundary of an input distribution for the second value as a first distribution value, (b) generating input samples within the set boundary of the input distribution, (c) obtaining a worst case point (WCP) for the input by performing machine learning on the generated input samples, and (d) repeatedly performing the steps (a) to (c) while shifting the boundary of the input distribution until the boundary of the input distribution reaches a minimum critical value.