Abstract: Described herein is a computer-implemented method. The method includes: providing digital images and respective formulas for coating compositions with known pigments and/or pigment classes associated with the respective digital images, classifying, using an image annotation tool, for each digital image, each pixel, by visually reviewing the respective digital image pixel-wise, providing, for each digital image, an associated pixel-wise annotated image, training a first neural network with the provided digital images as input and the associated pixel-wise annotated images as output, making the trained first neural network available for applying the trained first neural network to at least one unknown input image of a target coating and for assigning a pigment label and/or a pigment class label to each pixel in the at least one unknown input image, and determining and/or outputting, for each unknown input image, a statistic of corresponding identified pigments and/or pigment classes, respectively.

Abstract: Disclosed herein is a method and a device that can measure an unknown target coating; can search, based on the measured data of the target coating, a database for one or more best matching coating formulas, i.e. one or more preliminary matching formulas, within the database; and that can refine the search using an image similarity metric between images of the one or more best matching coating formulas on the one side and images of the target coating on the other side, using deep learning techniques.

Abstract: Disclosed herein is a computer-implemented method, a respective device, and a non-transitory computer-readable medium. The method includes: obtaining color values, texture values and digital images of a target coating, retrieving from a database one or more preliminary matching formulas based on the color and/or texture values obtained for the target coating, determining sparkle points within the respective obtained images and within the respective images associated with the one or more preliminary matching formulas, creating subimages of each sparkle point from the respective images, providing the created subimages to a convolutional neural network, the convolutional neural network being trained to correlate a respective subimage of a respective sparkle point with a pigment and/or pigment class, and determining, based on an output of the neural network, at least one of the one or more preliminary matching formulas as the formula(s) best matching the target coating.

Abstract: A lifting assembly can lift a substrate from a substrate support and transport the substrate. The lift assembly has a hoop sized to fit about a periphery of the substrate support, and a pair of arcuate fins mounted on the hoop, each arcuate fin comprising a pair of opposing ends having ledges that extend radially inward, each ledge having a raised protrusion to lift a substrate so that the substrate contacts substantially only the raised protrusion, thereby minimizing contact with the ledge, when the pair of fins is used to lift the substrate off the substrate support. The lifting assembly and other process chamber components can have a diamond-like coating having interlinked networks of (i) carbon and hydrogen, and (ii) silicon and oxygen. The diamond-like coating has a contact surface having a coefficient of friction of less than about 0.3, a hardness of at least about 8 GPa, and a metal concentration level of less than about 5×1012 atoms/cm2 of metal.