A method for carrying out a cyclical manufacturing process produces parts within a predefined quality tolerance. After at least one process adjustment variable is changed, a quality feature of the parts produced with a changed process adjustment variable is checked against the range of the quality tolerance of the produced parts. A process characteristic variable zone is formed in an automated manner using at least one determined process characteristic variable variant that is process-stable and for which the process adjustment variable produces acceptable parts.

An initial inspection or critical dimension measurement can be made at various sites on a wafer. The location, design clips, process tool parameters, or other parameters can be used to train a deep learning model. The deep learning model can be validated and these results can be used to retrain the deep learning model. This process can be repeated until the predictions meet a detection accuracy threshold. The deep learning model can be used to predict new probable defect location or critical dimension failure sites.

A machined surface quality evaluation device includes a machine learning device that learns a result of evaluation on machined surface quality of a workpiece by an observer which correspond to an inspection result on the machined surface quality of the workpiece. The machine learning device observes the inspection result on the machined surface quality of the workpiece as a state variable, acquires label data indicating the result of the evaluation on the machined surface quality of the workpiece by the observer, and learns the state variable and the label data in a manner such that they are correlated each other.

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.

An automated equipment installation verification system to automatically verify correctness of installation actions of an operative installing an item of equipment, the system comprising: a logic unit (804) that executes a rule engine to apply at least one rule to a measure of correctness of each of a plurality of physical acts performed by the operative according to a sequence of stepwise actions to be performed by the operative in the installation of the equipment to determine a degree of correctness of an installation of the equipment, each measure of correctness being determined by a classifier trained to determine a degree of correctness of a respective act based on sensor data corresponding to the act.