An information processing device includes: a memory; and a processor coupled to the memory and configured to: learn a classification rule that classifies an abnormal degree of a production facility from a text feature amount based on the text feature amount obtained from a number of texts included in a plurality of pieces of log data obtained in a predetermined process of the production facility and production history information of the production facility; extract a text feature amount of log data to be monitored obtained in the predetermined process of the production facility; and determine an abnormal degree of the production facility when the log data to be monitored is obtained based on the text feature amount and the classification rule.

Provided is an information processing apparatus performing: storing traceability information that is information which results from associating an apparatus that processes the component, an operating person who is in charge of the processing, and a timing at which the processing is performed, with each other; storing processing situation information that is information that results from associating a situation of the processing of the component and a timing at which the processing is performed, with each other; generating a combination at the same timing, of the processing, the apparatus or the operating person, and the situation of the processing, as an object integration data, based on the traceability information and the processing situation information; and outputting contents of the generated object integration data.

An apparatus and method for determining a target adjustment route for a preset control condition set of a production line are provided. The apparatus establishes at least one candidate adjustment route for the preset control condition set according to the historical control condition sets. Each candidate adjustment route includes at least one adjustment control condition set arranged in an adjustment order. Each adjustment control condition set is one of the historical control condition sets. Within the same candidate adjustment route, the historical yield related values corresponding to the adjustment control condition sets are all greater than the preset yield related value and increase in the adjustment order. Within the same candidate adjustment route, the numbers of the adjustment control condition(s) included in the adjustment sets increase in the adjustment order. The apparatus selects one of the candidate adjustment route(s) as the target adjustment route.

An apparatus and method for determining a target adjustment route for a preset control condition set of a production line are provided. The apparatus establishes at least one candidate adjustment route for the preset control condition set according to the historical control condition sets. Each candidate adjustment route includes at least one adjustment control condition set arranged in an adjustment order. Each adjustment control condition set is one of the historical control condition sets. Within the same candidate adjustment route, the historical yield related values corresponding to the adjustment control condition sets are all greater than the preset yield related value and increase in the adjustment order. Within the same candidate adjustment route, the numbers of the adjustment control condition(s) included in the adjustment sets increase in the adjustment order. The apparatus selects one of the candidate adjustment route(s) as the target adjustment route.

One or more processors receive operational data. The operational data includes two or more fields that contain an identical type of data as two or more fields within a master table. One or more processors generate a plurality of first unique identifiers from the two or more fields within the operational data and the master table. One or more processors determine a match between a pair of the plurality of first unique identifiers. The match is between a first unique identifier included in a operational data record and a first unique identifier included in a master table record. One or more processors determine a second unique identifier from the master table record. One or more processors generate an operational table record that includes the second unique identifier and at least a portion of the operational data.

The embodiments herein disclose methods and systems for predicting health of products in a manufacturing process. A method includes determining at least one of a dynamic data and a static data of at least one product from a manufacturing process steps. Further, the method includes determining and filtering at least one of a gradual change, an abrupt change and a similar data present in the at least one of the dynamic data and the static data. Further, the method includes converting the filtered at least one of the dynamic data and the static data of the at least one product into a common data format. The common data format can be stored in a common hyperspace. Further, the method includes predicting a health of the at least one product based on the common data format and the at least one product historical health information received from an apriori computer.

Systems and processes herein may be configured to correlate manufacturing parameters and performance feedback parameters with individual absorbent articles manufactured by a converting apparatus. Embodiments of the systems herein may include inspection sensors configured to inspect substrates and/or component parts advancing along the converting line and communicate inspection parameters to a controller and historian. The systems may also include process sensors configured to monitor equipment on the converting line and communicate process parameters to the controller and historian. The systems herein may also be adapted to receive performance feedback parameters based on the packaged absorbent articles. The systems may correlate inspection parameters, process parameters, and/or performance feedback parameters with individual absorbent articles produced on the converting line. The controller may also be configured to perform various functions based on the performance feedback parameters.

The present invention provides methods and systems for manufacturing process control of photovoltaic products. Some embodiments relate to a method for tracking wafers for photovoltaic products with respect to which production tool processed them and their position within that production tool. Some embodiments relate to measuring and characterizing the critical-to-quality parameters of the partially-finished photovoltaic products emerging from the production tool in question. Some embodiments relate to display and visualization of the measured parameters on a computer screen, such that the parameters of each production unit can be directly observed in the context of which production tools processed them, which location within a specific production tool they were located in during processing, and which batch, or in the case of continuous processing, what time, the unit(s) was/where processed.

Systems and methods for monitoring stability of a wafer inspection recipe over time are provided. One method includes collecting inspection results over time. The inspection results are generated by at least one wafer inspection tool while performing the wafer inspection recipe on wafers at different points in time. The method also includes identifying abnormal variation in the inspection results by comparing the inspection results generated at different times to each other. In addition, the method includes determining if the abnormal variation is attributable to the wafers, the wafer inspection recipe, or one or more of the at least one wafer inspection tool thereby determining if the wafer inspection recipe is stable over time.

An apparatus and a method thereof for determining a control condition set of a production line. The apparatus divides several historical control condition sets into several groups, wherein the historical control conditions corresponding to the same control factor are the same in each group. For each group, the apparatus calculates a measurement of central tendency according to the historical yield related values in the group. The apparatus decides a subset of the groups. For each group in the subset, the apparatus calculates a degree of variation and a number regarding the different control conditions between the control condition set and the group. The apparatus calculates weight scores. Based on the measurements of central tendency and the weight scores, the apparatus selects one of the groups as a selected group and assigns the historical control conditions of the selected group as the control conditions of the control condition set.