Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: Test structures for wafers are disclosed. A device may include a silicon wafer including a number of die and a scribe area between two die of the number of die. The scribe area may include one or more test structures. The test structures may include a p-doped region and an n-doped region adjacent to the p-doped region. The test structures may also include a first contact electrically coupled to the p-doped region and a second contact electrically coupled to the n-doped region. The second contact may be proximate to the first contact. Associated devices, systems, and methods are also disclosed.

Abstract: Stacks of electrically resistive materials and related apparatuses, electrical systems, and methods are disclosed. An apparatus includes one or more resistor devices including a substrate, first and second electrically resistive materials, and an electrically insulating material between the first and second electrically resistive materials. The substrate includes a semiconductor material. A stepped trench is defined in the substrate by sidewalls and horizontal surfaces of the semiconductor material. The first electrically resistive material and the second electrically resistive material are within the stepped trench.

Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

Abstract: A semiconductor light emitting device includes a semiconductor light emitting structure including first and second conductivity type semiconductor layers, and an active layer disposed therebetween, first and second electrodes connected to the first and second conductivity type semiconductor layers, respectively, and a fine pattern for light extraction, formed on a light emitting surface from which light generated from the active layer is emitted. The fine pattern for light extraction is formed as a graded refractive index layer having a refractive index which decreases with vertical distance from the light emitting surface.