There is provided a technique that includes (a) receiving a recipe for measuring an object to be measured; (b) calculating an estimated time when measuring the object according to a setting order of respective measurement points set in the recipe; (c) changing the setting order of the respective measurement points set in the recipe according to a measurement pattern for measuring the object and calculating an estimated time when measuring the object according to the changed setting order; (d) selecting the setting order in which the estimated time is the shortest among the estimated times calculated in (b) and (c); and (e) measuring the object in the selected setting order.

A semiconductor equipment calibration system includes a remote calibration system and a calibration apparatus. The cloud-based remote calibration system includes a process tool calibration database storing tool calibration data for a large number of semiconductor process tools. The calibration apparatus is configured to be deployed adjacent to a semiconductor process tool and to receive, from the cloud-based remote calibration system, tool calibration data associated with the semiconductor process tool. The calibration apparatus includes a sensor support arm, a sensor coupled to the sensor support arm and configured to generate sensor data indicating a configuration of a semiconductor process tool, and a control circuit configured to generate diagnostic data by comparing the sensor data to the tool calibration data.

A method and device for producing contact metallizations on terminal faces of wafers by a manipulator unit for handling the wafers and a plurality of work stations each having a processing space for receiving the wafers, the plurality of work stations including a depositing station, which has a processing space for receiving a solution of contact metal dissolved in a carrier liquid for deposition on the terminal faces of the wafers, detecting a measured value of an object property of a wafer or of a work station by a sensor, and determining a prioritization of equipping the processing spaces or a dwell time of the wafers in a processing space by querying the measured value in a databank or by means of inference using a statistic model while entering the measured value, generating a control dataset for the manipulator unit; and handling the wafers according to the control dataset.

A transport facility includes a plurality of transport vehicles that travel in a travel area to transport articles, and a control system that controls the plurality of transport vehicles. The travel area includes a plurality of rechargeable points for recharging the plurality of transport vehicles, and a plurality of transfer points at each of which the plurality of transport vehicles transfer the articles. The control system performs a relay transport process. In the relay transport process, a relay point is selected from the plurality of transfer points, and a recharge point for recharging a first transport vehicle is selected from the plurality of rechargeable points. The relay point is a transfer point closest to the unload point on a transport path of the transport vehicle. The recharge point is selected from rechargeable points of the plurality of rechargeable points within a selection range from the relay point.

A selection of manufacturing equipment associated with a current fabrication process of a manufacturing environment is received. A respective eco-efficiency model corresponding to at least one of the selected manufacturing equipment is identified from a set of eco-efficiency models. Each of the set of eco-efficiency models represents a prior environmental resource consumption of a prior fabrication process involving a respective manufacturing equipment component. Values for one or more process parameters for the current fabrication process that will reduce environmental resource consumption of the current fabrication process when run using the selected manufacturing equipment are determined based on the identified respective eco-efficiency model. The determined values for the one or more process parameters are applied to the current fabrication process.

A method includes identifying at least one of historical data associated with historical substrate lots processed by substrate processing tools in a substrate processing facility or simulated data for simulated substrate lots processed by simulated substrate processing tools. The method further includes generating features from the at least one of the historical data for the historical substrate lots or the simulated data for the simulated substrate lots. The method further includes training a machine learning model with data input comprising the features to generate a trained machine learning model. The trained machine learning model is capable of generating one or more outputs indicative of one or more corrective actions to be performed in the substrate processing facility.

A control device of a substrate processing apparatus is configured to execute: calculating patterns for changing an order of loading to the substrate processing apparatus for multiple substrates loaded to the substrate processing apparatus; generating, for each obtained pattern, a time table in which process end times in the polishing device, the cleaning device, and the transport device are associated, so that an idling state does not occur from a time when the substrates are loaded to the substrate processing apparatus until a cleaning process ends; selecting a time table with a shortest time from a time when a process of a substrate initially loaded to the substrate processing apparatus starts until a process of a lastly loaded substrate ends in the obtained time tables; and controlling timings of loading the substrates to the substrate processing apparatus based on the selected time table.

A wafer map generation device includes a dimension reducer and a map generator. The dimension reducer is configured to receive a plurality of polarization matrices associated with a plurality of target positions of a target wafer substrate and to generate a plurality of reduction result data through dimension reduction of one or more elements of each of the plurality of polarization matrices. The map generator is configured to generate a wafer map associated with the target wafer substrate based on the plurality of reduction result data.

A semiconductor process transport apparatus including a drive section with at least one motor, an articulated arm coupled to the drive section for driving articulation motion, a machine controller coupled to the drive section to control the at least one motor moving the articulated arm from one location to a different location, and an adapter pendant having a machine controller interface coupling the adapter pendant for input/output with the machine controller, the adapter pendant having another interface, configured for connecting a fungible smart mobile device having predetermined resident user operable device functionality characteristics, wherein the other interface has a connectivity configuration so mating of the fungible smart mobile device with the other interface automatically enables configuration of at least one of the resident user operable device functionality characteristics to define an input/output to the machine controller effecting input commands and output signals for motion control of the articulated arm.

Technologies related to production scheduling within semiconductor fabrication plant(s) are described. Processing chambers may receive requests to complete a workload over a period of time. The workload may be scheduled across the processing chambers to maximize an aggregate time that the processing chambers are in a sleep mode.