6 results about "Group process" patented technology

The invention relates to an intelligent conference grouping method and conference grouping software, the method is used for a grouping subsystem in the conference grouping software, and the grouping subsystem is used for receiving a grouping instruction of a student to realize intelligent grouping; the conference intelligent grouping method comprises the following steps: S1, in response to a grouping instruction, obtaining an enrollment student set and characteristic attributes; s2, based on the grouping instruction and the enrollment student set, obtaining a corresponding target grouping number and the number of students of each category; and S3, generating an intelligent conference grouping result according to the number of students in each category and the target grouping number in combination with a preset sorting rule. In the existing conference grouping work, a worker needs to manually export a table and then manually group the table according to conditions such as gender, application type, payment condition and the like, and the whole process is time-consuming and labor-consuming; on the basis of the scheme, intelligent grouping and supplementary grouping can be carried out according to different conditions in different conference scenes, so that subsequent follow-up service is facilitated.

Disclosed herein is a method for monitoring the stability of process systems in semiconductor manufacturing using digital twins and neural networks. Digital twins of both individual and group process systems are created to identify sources of subsystem parameter changes over time and assess their precise impact on process performance. This method enhances operational efficiency, uniformity, and precision in complex semiconductor manufacturing processes.

The application discloses a system and method for process system matching and optimization based on digital twinning and neural networks. The method constructs digital twins of single and group process systems, and the group digital twins can guide the matching of the group process systems. The neural network trained by simulation data and measurement data can improve the calculation efficiency and realize accurate matching and compatibility of the process systems.

The present application relates to the technical field of computer-aided process planning, and particularly relates to a closed-loop recovery method and system for multi-intelligent group process planning under manufacturing constraints, comprising: formalizing manufacturing constraints into quadruples with severity weights, driving the sequential execution and calling of constraint checking of feature extraction intelligent groups, macro process planning intelligent groups, specific process planning intelligent groups and process optimization evaluation intelligent groups; when there is a violation, locating a root cause intelligent group by traversing upstream from a constraint responsibility intelligent group along a directed dependency graph, determining a rollback target and injecting a repair prompt according to a severity weight and a rollback cost heuristic; verifying again after the rollback target intelligent group is regenerated, iteratively triggering root cause tracking and adaptive rollback until passing. The present application realizes accurate attribution and efficient closed-loop recovery under manufacturing constraint violations, improves constraint satisfaction rate and reduces rollback steps.

Disclosed herein is a method for matching process systems in semiconductor manufacturing by utilizing digital twins and neural networks. Digital twins of individual and group process systems are constructed, with the group digital twin guiding the matching of process systems within the group. Neural networks, trained on simulation and measured data, enhance computational efficiency, enabling precise matching and compatibility.

A method for monitoring stability of a semiconductor manufacturing process system using digital twinning and neural networks is disclosed. In the method, digital twins of both individual and group process systems are created to identify changes in subsystem parameters over time and assess their impact on the accuracy of process performance. The method improves the efficiency, uniformity, and accuracy of complex semiconductor manufacturing processes.