Systems, methods, and devices may be configured to detect and remediate component failures in three-dimensional object printers (10, 10a-f) preemptively. For example, systems may include: (a) a plurality of printers (10, 10a-f) each configured to produce three-dimensional objects (13), each printer including: (i) a plurality of subsystems; and (ii) at least one sensor; and (b) processor(s) (41, 42) and memory resource(s) (21) storing an inventory of available replacement components for at least some of said subsystems. The one or more memory resources may (21) store instructions that may cause the one or more processors to: (i) identify a predetermined pattern in data sensed during a process of producing a three-dimensional object by a sensor of a printer as an indicator of likely failure of a subsystem or component thereof; and (ii) assign a component in inventory to said printer based on a unique identifier of the printer and the indicator of likely failure identified in the signal.

A method for categorizing a substrate subject to a semiconductor manufacturing process including multiple operations, the method including: obtaining values of functional indicators derived from data generated during one or more of the multiple operations on the substrate, the functional indicators characterizing at least one operation; applying a decision model including one or more threshold values to the values of the functional indicators to obtain one or more categorical indicators; and assigning a category to the substrate based on the one or more categorical indicators.

The feedback control device takes information regarding a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object; comprising:

a first control unit that takes information regarding the control deviation as input, and outputs a first control amount for the controlled object; a second control unit that takes information regarding the control deviation as input and outputs a second control amount for the controlled object, and in which a parameter for calculating the second control amount is determined by machine learning;

an operation unit that operates the controlled object using the first control amount output from the first control unit and the second control amount output from the second control unit; and a sampling unit for thinning out at a predetermined period information regarding the control deviation input to the second control unit.

A method for configuring a semiconductor manufacturing process, the method including: obtaining a first value of a first parameter based on measurements associated with a first operation of a process step in the semiconductor manufacturing process and a first sampling scheme; using a recurrent neural network to determine a predicted value of the first parameter based on the first value; and using the predicted value of the first parameter in configuring a subsequent operation of the process step in the semiconductor manufacturing process.

A method including: determining a value of a characteristic of a patterning process or a product thereof, at a current value of a processing parameter; determining whether a termination criterion is met by the value of the characteristic; if the termination criterion is not met, determining a new value of the processing parameter from the current value of the processing parameter and a prior value of the processing parameter, and setting the current value to the new value and repeating the determining steps; and if the termination criterion is met, providing the current value of the processing parameter as an approximation of a value of the processing parameter at which the characteristic has a target value.

A technique to generate predicted data for control or monitoring of a production process to improve a parameter of interest. Context data associated with operation of the production process is obtained. Metrology/testing is performed on the product of the production process, thereby obtaining performance data. A context-to-performance model is provided to generate predicted performance data based on labeling of the context data with performance data. This is an instance of semi-supervised learning. The context-to-performance model may include the learner that performs semi-supervised labeling. The context-to-performance model is modified using prediction information related to quality of the context data and/or performance data. Prediction information may include relevance information relating to relevance of the obtained context data and/or obtained performance data to the parameter of interest. The prediction information may include model uncertainty information relating to uncertainty of the predicted performance data.

A method for configuring a semiconductor manufacturing process, the method including: obtaining a first value of a first parameter based on measurements associated with a first operation of a process step in the semiconductor manufacturing process and a first sampling scheme; using a recurrent neural network to determine a predicted value of the first parameter based on the first value; and using the predicted value of the first parameter in configuring a subsequent operation of the process step in the semiconductor manufacturing process.

A system for monitoring a process step during manufacturing of an assembly sheet includes a detection camera configured to capture a first image of the assembly sheet, the first image including a locating feature of the assembly sheet, a vacuum hold-down device for selectively inhibiting advancement of the assembly sheet along a process step line for a predetermined measurement time, and a measurement camera configured to capture a second image of the assembly sheet responsive to the vacuum hold-down device inhibiting advancement of the assembly sheet, the second image including one or more features of a coupon of the assembly sheet.

Examples of synchronized parallel tile computation techniques for large area lithography simulation are disclosed herein for solving tile boundary issues. An exemplary method for integrated circuit (IC) fabrication comprises receiving an IC design layout, partitioning the IC design layout into a plurality of tiles, performing a simulated imaging process on the plurality of tiles, generating a modified IC design layout by combining final synchronized image values from the plurality of tiles, and providing the modified IC design layout for fabricating a mask. Performing the simulated imaging process comprises executing a plurality of imaging steps on each of the plurality of tiles. Executing each of the plurality of imaging steps comprises synchronizing image values from the plurality of tiles via data exchange between neighboring tiles.

The current disclosure describes techniques for managing vertical alignment or overlay in semiconductor manufacturing using machine learning. Alignments of interconnection features in a fan-out WLP process are evaluated and managed through the disclosed techniques. Big data and neural networks system are used to correlate the overlay error source factors with overlay metrology categories. The overlay error source factors include tool related overlay source factors, wafer or die related overlay source factors and processing context related overlay error source factors.