In one embodiment, a system includes a wafer handling system, processing components, a controller, a virtual assistant, a natural language processing (NLP) engine, and a data-analytics engine. The wafer handling system is configured to hold one or more wafers for processing. The processing components is configured to physically treat the one or more wafers. The controller is configured to operate the processing components. The virtual assistant, in communication with the NLP engine, is configured to receive a user query from a user, understand an intent or context of the user query, and provide a context-specific response to the user query. The data-analytics engine is configured to generate and provide analytical data relating to the user query based on data collected from a plurality of data sources via one or more communication protocols.

This control system is provided with a controller, a controller which is a loopback communication device, a first communication route, and a second communication route. The controller generates and transmits control frames to slave devices. The controller performs loopback communication of the control frames. In the first communication route, multiple slave devices are connected between the controller and the controller using communication cables. In the second communication route, the first controller and the second controller are connected over a communication cable.

In one embodiment, a condition monitoring circuit can include a circuit controller and a node. The node can include a gate controller, a node controller and one or more gates. The node can be configured to detachably couple to a bus of a monitoring system associated with an industrial machine. The circuit controller can be configured to identify an operating parameter associated with the industrial machine. The gate controller can be configured to transfer, via the one or more gates, one or more data packets including data characterizing the operating parameter from the bus in the monitoring system. The one or more gates can be configured to prevent transfer of an outgoing data packet to the bus via the node.

In a production system, a control unit controls a first apparatus configured to start to operate after a waiting time and a second apparatus configured to starting to operation after a waiting time. A first sensor outputs a value that changes in response to operation of the first apparatus. A second sensor outputs a value that changes in response to operation of the second apparatus. The control unit compares a first time period from a starting of the operation of the first apparatus to an occurrence of a change in the value of the first sensor with a predetermined first threshold value, and compares a second time period from a starting of the operation of the second apparatus to an occurrence of a change in the value of the second sensor with a predetermined second threshold value.

A dimension tolerance determining method and a system are disclosed. The method includes: determining initial tolerances of parts in assembled product; generating a plurality of tolerance data sets according to initial tolerances of parts, each tolerance data set containing a calculated part tolerance of each part; inputting the plurality of tolerance data sets into a key parameter generation module to generate a plurality of key parameters corresponding to the assembled product, wherein each key parameter corresponds to one tolerance data set; when the key parameters are located within a design range, calculating a plurality of assembly yield rates based on the tolerance data sets corresponding to the key parameters located within the design range; and selecting the tolerance data set corresponding to one of the assembly yield rates as the tolerances of the parts in the assembled product; wherein the key parameter generation module includes a neural network model.

The confidentiality of data is maintained in the case of outsourcing analysis of an operation state of a facility. Degree-of-relevance information indicating a degree of relevance in operation between a plurality of apparatuses included in a facility is stored. At the occurrence of a failure in a first apparatus included in the plurality of apparatuses, a second apparatus having a degree of relevance in operation to the first apparatus equal to or greater than a preset first threshold is selected from the plurality of apparatuses based on the degree-of-relevance information, and the log data relating to operation states of the first apparatus in which a failure has occurred and the selected second apparatus are selectively read from a first memory storage, and the read log data is transmitted to a data user via a network.

The confidentiality of data is maintained in a case where analysis of an operation state of a facility is entrusted to the outside. Time difference information in operation between the manufacturing apparatuses (RB1), (RB2), . . . , arranged on the production line (LN) is stored for each of the apparatuses, in an inter-apparatus time difference information storage unit (33). At the occurrence of a failure in one of the manufacturing apparatuses (RB1), (RB2), . . . , the operation estimation unit (13) selects, from among the peripheral apparatuses on the upstream side and the downstream side with respect to the manufacturing apparatus concerned, one on the upstream side and one on the downstream side, estimates an operation period of each selected peripheral apparatus associated with the failure, reads log data corresponding to the estimated operation period from the operation history storage unit (31), and transmits the read piece to the external support center (SC).

A system and method for facilitating predictive maintenance of an equipment is disclosed. The system may include a data capturer, a plurality of edge computing nodes and a cloud computing device. Each edge computing node may include a first processor. The cloud computing device may include a second processor. The first processor may receive the raw input data from the data capturer and may process the raw input data to obtain a representative data. The representative data may include an insight pertaining to a deviation in the at least one variable and a corresponding remedial action to be taken to correct the deviation. The deviation may be related to a deterioration in the condition of the equipment. The respective edge computing node may facilitate a regulation of the deviation by performing an automated actuation based on the corresponding remedial action.

A drawing apparatus according to the embodiment includes a chamber configured to house a processing target; a drawing part configured to draw a predetermined pattern on the processing target with a charged particle beam; a resistance measuring part configured to measure a resistance value of the processing target via a grounding member grounding the processing target in the chamber; a receiver configured to receive earthquake information; a controller configured to stop a drawing process in the chamber when the receiver receives the earthquake information; and an arithmetic processor configured to determine whether the processing target is grounded on a basis of the resistance value from the resistance measuring part, wherein the controller resumes the drawing process when the arithmetic processor determines that the processing target is grounded after the drawing process is stopped.

A managing apparatus includes: a data collecting unit that collects at least quality information on a workpiece having been machined by a first machine tool and operational information on other machine, an analyzing unit that performs an analysis for determining correlation between the quality information on a workpiece having been machined by the first machine tool and the operational information on the other machine, an operation plan making unit that makes an operation plan, based on the correlation determined by the analyzing unit, for imposing a restriction on an operation of the other machine to reduce vibrations to be transmitted to the first machine tool during high-precision machining performed by the first machine tool, and an operation instruction providing unit that provides an operation instruction to the other machine based on the operation plan made by the operation plan making unit.