Abstract: Methods and systems for detecting and correcting substrate process drift using machine learning are provided. Data associated with processing each of a first set of substrates at a manufacturing system according to a process recipe is provided as input to a trained machine learning model. One or more outputs are obtained from the trained machine learning model. An amount of drift of a first set of metrology measurement values for the first set of substrates from a target metrology measurement value is determined from the one or more outputs. Process recipe modification identifying one or more modifications to the process recipe is also determined. For each modification, an indication of a level of confidence that a respective modification to the process recipe satisfies a drift criterion for a second set of substrates is determined.

Abstract: An article, apparatus, and method for detecting an etch material selectivity is provided. A device including a first layer and a second layer is placed in a processing chamber. The first layer includes a first sense material deposited on a first portion of the device and a second sense material deposited on a second portion of the device. The second layer deposited on the first layer includes an etch material. During an etch process based on an initial set of etch parameter settings, a first amount of time to etch the second layer at the first portion of the device and a second amount of time to etch the second layer at the second portion of the device are measured. A first etch rate and a second etch rate of the processing chamber is determined based on the measured first amount of time, the measured second amount of time, and a thickness of the second layer.

Abstract: An article, apparatus, and method for detecting an etch rate uniformity in a processing chamber of an electronics processing system is provided. A device is placed in a processing chamber of an electronics processing system. The device includes a first layer deposited on a surface of the device and a second layer deposited on the first layer. The first layer is composed of a first sense material and the second layer is composed of an etch material. During an etch process at the processing chamber, a first amount of time from an initiation of the etch process to a detection of a first indication of completion of etching of the second layer at a first portion of the surface of the device is measured. The etch process etches the second layer of the device based on an initial set of etch parameter settings. A first etch rate of the processing chamber is determined based on the measured first amount of time and a thickness of the second layer.

Abstract: A method for a substrate measurement subsystem is provided. An indication is received that a substrate being processed at a manufacturing system has been loaded into a substrate measurement subsystem. First positional data of the substrate within the substrate measurement subsystem is determined. One or more portions of the substrate to be measured by one or more sensing components of the substrate measurement subsystem are determined based on the first positional data of the substrate and a process recipe for the substrate. Measurements of each of the determined portions of the substrate are obtained by one or more sensing components of the substrate measurement subsystem. The obtained measurements of each of the determined portions of the substrate are transmitted to a system controller.

Abstract: A method for training a machine learning model to predict metrology measurements of a current substrate being processed at a manufacturing system is provided. Training data for the machine learning model is generated. A first training input including historical spectral data and/or historical non-spectral data associated with a surface of a prior substrate previously processed at the manufacturing system is generated. A first target output for the first training input is generated. The first target output includes historical metrology measurements associated with the prior substrate previously processed at the manufacturing system. Data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including a first target output.

Abstract: A method for determining whether to modify a manufacturing process recipe is provided. A substrate to be processed at a manufacturing system according to the first process recipe is identified. An instruction to transfer the substrate to a substrate measurement subsystem to obtain a first set of measurements for the substrate is generated. The first set of measurements for the substrate is received from the substrate measurement subsystem. An instruction to transfer the substrate from the substrate measurement subsystem to a processing chamber is generated. A second set of measurements for the substrate is received from one or more sensors of the processing chamber. A first mapping between the first set of measurements and the second set of measurements for the substrate is generated. The first set of measurements mapped to the second set of measurements for the substrate is stored.

Abstract: A system includes a transparent crystal, at least part of which is embedded within a wall and a liner of a processing chamber. The transparent crystal has a proximal end and a distal end, the distal end having a distal surface exposed to an interior of the processing chamber. A transparent thin film is deposited on the distal surface and has chemical properties substantially matching those of the liner. A light coupling device is to: transmit light, from a light source, through the proximal end of the transparent crystal, and focus, into a spectrometer, light received reflected back from a combination of the distal surface, a surface of the transparent thin film, and a surface of a process film layer deposited on the transparent thin film. The spectrometer is to detect a first spectrum within the focused light that is representative of the process film layer.