Abstract: A thickness estimation method may include: obtaining a test spectrum image; obtaining test spectrum data; measuring a thickness of a test layer formed on the test substrate at the plurality of positions; generating a regression analysis model using a correlation between the thickness of the test layer and the test spectrum data; obtaining a spectrum image; and estimating a thickness of a target layer over the entire area of the semiconductor substrate by applying the spectrum image to the regression analysis model. The thickness corresponding to the entire area of the semiconductor substrate that is being transferred is estimated using the thickness estimation method according to an exemplary embodiment in the present disclosure, such that whether or not processing is normally performed may be examined without requiring a separate time. In addition, an examination result may be feedbacked to processing equipment to improve production yield.

Abstract: A thickness estimating apparatus includes a transfer robot, a light source, a camera, a memory and a controller. The memory stores a thickness predicting model generated based on a data set including a thickness of at least one of a test wafer corresponding to the wafer or a test element layer formed on the test wafer, and the thickness predicting model being trained to minimize a loss function of the data set. The controller applies pixel data, which is acquired from at least one pixel selected from a plurality of pixels included in a captured image, to the thickness predicting model, to predict a thickness of at least one of the wafer or an element layer formed on the wafer in a position corresponding to a position of the selected pixel.

Abstract: A thickness estimation method may include: obtaining a test spectrum image; obtaining test spectrum data; measuring a thickness of a test layer formed on the test substrate at the plurality of positions; generating a regression analysis model using a correlation between the thickness of the test layer and the test spectrum data; obtaining a spectrum image; and estimating a thickness of a target layer over the entire area of the semiconductor substrate by applying the spectrum image to the regression analysis model. The thickness corresponding to the entire area of the semiconductor substrate that is being transferred is estimated using the thickness estimation method according to an exemplary embodiment in the present disclosure, such that whether or not processing is normally performed may be examined without requiring a separate time. In addition, an examination result may be feedbacked to processing equipment to improve production yield.

Abstract: A thickness estimating apparatus includes a transfer robot, a light source, a camera, a memory and a controller. The memory stores a thickness predicting model generated based on a data set including a thickness of at least one of a test wafer corresponding to the wafer or a test element layer formed on the test wafer, and the thickness predicting model being trained to minimize a loss function of the data set. The controller applies pixel data, which is acquired from at least one pixel selected from a plurality of pixels included in a captured image, to the thickness predicting model, to predict a thickness of at least one of the wafer or an element layer formed on the wafer in a position corresponding to a position of the selected pixel.