A computer-implemented method and computing system are provided for failure prediction of a batch of manufactured objects. The method includes classifying, by a processor using a simulation, a set of samples with uniformly distributed parameter values, to generate sample classifications for the batch of manufactured objects. The method further includes determining, by the processor, a centroid of failing ones of the samples in the set, based on the sample classifications. The method also includes generating, by the processor, a new set of samples with a distribution around the centroid of the failing ones of the sample in the set. The method additionally includes populating, by the processor, a nearest neighbor vector space using the new set of samples. The method further includes classifying, by the processor, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric.

A method comprises determining that a batch generated in an industrial process (IP) is anomalous at a sample point k during the batch, the batch is ongoing; determining a process variable (PV) of the IP based on a variable contribution of the PV towards the batch being anomalous at the sample point k; determining a recommended value of the PV based on an anomaly metric corresponding to the sample point k of an assessment batch, the assessment batch is created based on sample(s) of the batch at the sample point k and the recommended value of the PV, the anomaly metric corresponding to the sample point k of the assessment batch is determined based on a T.sup.2-statistic metric corresponding to the sample point k and a Q-statistic metric corresponding to the sample point k of the assessment batch; and adjusting the IP based on the recommended value of the PV.

To determine a quality indicator of production batch-run of a production process, a computer compares time-series with multi-source data from a reference batch-run and time-series with multi-source data from the production batch-run. Before comparing, the computer converts multi-variate time-series to uni-variate time-series, by first multiplying data values of source-specific uni-variate time-series with source-specific factors from a conversion factor vector and second summing up the multiplied data values according to discrete time points. The source-specific factors of the conversion factor vector are obtained earlier by processing reference data, including the determination of characteristic portions of the time-series, converting, aligning by time-warping and evaluating displacement in time between characteristic portions before alignment and after alignment.

An illustrative method includes a batch analytic system receiving batch data of a batch generated in an industrial process, wherein the batch data includes a set of samples associated with the batch, the batch is complete and has a first batch length, determining a reference batch based on a plurality of non-anomalous batches generated in the industrial process, wherein each non-anomalous batch has a same second batch length, generating a batch representation of the batch based on the batch data of the batch and the reference batch, wherein the batch representation of the batch aligns with the reference batch and has the second batch length associated with the reference batch, and performing an operation using the batch representation of the batch.

A process control method is provided for performing a deposition process on a plurality of wafers of a batch. The process control method includes: deciding a placement location of the wafers of the batch according to the history information of a tool and the product information of the batch; calculating a target value of each placement location according to the placement location of the wafers of the batch and the history information of the tool; calculating a process parameter according to the history information of the tool, the product information of the batch, and the target value of each placement location; and performing a deposition process according to the placement location of the wafers of the batch and the process parameter.

Methods and apparatus to define stages for multi-variate batch control analytics are disclosed. An example method includes determining, with the processor, a current stage in a current batch process based on a current value of a batch stage parameter. The current value of the batch stage parameter determined based on process control data associated with process parameters in the current batch process. The current stage determined independent of batch events defined by at least one of a start or an end of procedures, unit procedures, operations, or phases in a batch recipe. The example method further includes applying, with the processor, a model to the current batch process, the model corresponding to the current stage.

To provide enhanced search capabilities in a process control system, a knowledge repository is generated that includes both contextual data and time series data. The contextual data organizes process plant-related data according to semantic relations between the process plant-related data and the process plant entities. When a user submits a process plant search query related to process plant entities within a process plant, search results are obtained by identifying a data set from the knowledge repository. The contextual data categorizes process parameters so that users can search for a particular process parameter category. Users can tag previous searches to execute them once again at a later time. Users can also execute queries for predicted or future states of process plant entities, batch queries regarding batch processes, soft sensor analytics and monitoring applications, parameter lifecycle applications, perturbation applications, step testing applications, or batch provisioning and scheduling applications using the knowledge repository.

This disclosure is directed to a mouse foot, which includes a body and a boss. The body has an adhesive surface and a top, and the adhesive surface and the top are arranged on opposite sides of the body. The boss is arranged on the top of the body. The boss has at least one convex part and at least one concave part. A height difference is defined between the convex part and the concave part adjacent to each other, and a rounded corner is formed on an edge of the convex part.

Described herein are methods for performing changover of a production line from a first product to a second product. One method involves assigning changover codes to the first product and second product and determining a wash type for the changover. A second method involves receiving a plurality of batch production orders for a plurality of products. Wash types for different sequences of the batch production orders are determined and a sequence is selected that reduces the amount of time for washing the line during changeovers. A third method involves transferring a product to a fill station prior to receiving quality control test results. The product is held at the fill station prior to the receipt of the test results. If the product passes the quality control test then the product is distributed by the fill station; otherwise, the product fails the quality control test, the product is quarantined.

A computer-implemented method for controlling and/or monitoring a production plant (110) is proposed. The production plant (110) comprises at least one process chain (112) comprising at least one batch process (114). The method comprises the following steps: a) at least one step of determining of input data (132), wherein the input data comprises at least one quality criterion and production plant layout data, wherein the step comprises retrieving the production plant layout data and receiving information relating to the quality criterion via at least one communication interface (158); b) at least one prediction step (134), wherein in the prediction step operating conditions for operating the production plant (110) are determined by applying at least one trained model (136) on the input data, wherein the trained model (136) is at least partially data-driven by being trained on sensor data from historical production runs; c) at least one control and/or monitoring step (140), wherein the operating conditions are provided.