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.

A computer system and computer-implemented method are described for controlling and monitoring a process to produce a chemical, pharmaceutical or biotechnological product. The method includes providing a database that stores sets of process parameters to control and monitor a plurality of processes performed in order to produce products, receiving a set of characterizing process parameters that characterize the process, identifying a first set process parameters from the stored sets of process parameters, and controlling and monitoring the process using a successful trajectory that includes a time-based profile of measurements.

The present disclosure provides a system and method for recommending one or more potential changes in operating parameters of a built environment. The one or more potential changes are associated with a plurality of energy load sources. The method includes a step of fetching a first set of statistical data associated with each of a plurality of energy consuming devices. The method includes another step of collecting a third set of statistical data associated with one or more ambient parameters of the built environment. The method includes yet another step of analyzing the first set of statistical data, the second set of statistical data and the third set of statistical data. In addition, the method includes yet another step of recommending the one or more potential changes for the energy consumption of each of the plurality of energy consuming devices.

The invention may be embodied in a system and method for monitoring and controlling feedback control in a manufacturing process, such as an integrated circuit fabrication process. The process control parameters may include translation, rotation, magnification, dose and focus applied by a photolithographic scanner or stepper operating on silicon wafers. Overlay errors are used to compute measured parameters used in the feedback control process. Statistical parameters are computed, normalized and graphed on a common set of axes for at-a-glance comparison of measured parameters and process control parameters to facilitate the detection of problematic parameters. Parameter trends and context relaxation scenarios are also compared graphically. Feedback control parameters, such as EWMA lambdas, may be determined and used as feedback parameters for refining the APC model that computes adjustments to the process control parameters based on the measured parameters.

Methods and computer program products for performing automatically determining when to shut down a fabrication tool, such as a semiconductor wafer fabrication tool, are provided herein. The methods include, for example, creating a measurement vector including process parameters of semiconductor wafers, creating a correlation matrix of correlations between measurements of parameters obtained of each wafer, creating autocorrelation matrixes including correlations between measurements of the parameter obtained for pairs of wafers; creating a combined matrix of correlation and autocorrelation matrixes, obtaining a T.sup.2 value from the measurement vector and combined matrix, and stopping a semiconductor wafer fabrication tool if the T.sup.2 value exceeds a critical value.

The present disclosure provides a system and method for recommending one or more potential changes in operating parameters of a built environment. The one or more potential changes are associated with a plurality of energy load sources. The method includes a step of fetching a first set of statistical data associated with each of a plurality of energy consuming devices. The method includes another step of collecting a third set of statistical data associated with one or more ambient parameters of the built environment. The method includes yet another step of analyzing the first set of statistical data, the second set of statistical data and the third set of statistical data. In addition, the method includes yet another step of recommending the one or more potential changes for the energy consumption of each of the plurality of energy consuming devices.

A method for monitoring a process in a semiconductor processing facility and a monitor system are provided. A plurality of wafers are processed according to a process. Data on the processing is collected, and the collecting includes, for each wafer of the plurality of wafers, determining that a processing event has occurred, and recording a time associated with the processing event. An amount of time between the recorded times is calculated for consecutively processed wafers. A set of control limits for the process is determined based on the calculated amounts of time. The set of control limits define a range of acceptable values for the amount of time. Second wafers are processed according to the process. A problem in the processing of the second wafers is identified based on the set of control limits. The problem is identified as the second wafers are being processed.

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.

A diagnostic device and method for monitoring the operation of a slave or ratio control loop in a meshed control structure of an automation system. The diagnostic device includes an evaluation device and a data memory for storing sequences of setpoint data and actual value data. The evaluation device determines a first dimension for the scatter of the actual-value data and a second dimension for the scatter of the setpoint data. A characteristic number (CPI.sub.Var, CPI.sub.Kas) for evaluating control quality is determined and/or displayed as a function of the ratio of the first dimension to the second dimension to enable an operator to evaluate the control loop status, permitting automated control loop evaluation of a fluctuating setpoint.