A multi-scenario performance index prediction method and system for semiconductor production lines

Abstract

The present invention relates to a multi-scenario performance index prediction method and system for semiconductor production lines, including: a production scene quantitative division module, driven by data, to quantitatively map the production line's WIP value, product average processing cycle, and utilization rate of each equipment, Divide the production line into three scenarios: light load, normal load, and heavy load; the main prediction network construction module uses the divided normal load data as sample data, and combines the deep neural network algorithm with the performance prediction of the semiconductor production line to construct a normal load scenario The prediction network under the following conditions; the building block of the multi-scenario prediction model, which applies the idea of ​​transfer learning to the production line prediction, and constructs the network under light-load and heavy-load scenarios according to the main prediction network that has been constructed, thus forming a multi-scenario prediction model. Compared with the prior art, the present invention quantitatively divides production line scenarios, can more accurately predict multiple production line performance indicators under different load scenarios, and can be used online.

Description

technical field [0001] The present invention relates to the technical field of intelligent manufacturing, in particular to a multi-scenario performance index prediction method and system for semiconductor production lines. Background technique [0002] The manufacturing industry is the main body of the national economy, the engine of rapid economic growth, and an important manifestation of comprehensive national strength. In order to ensure competitiveness, manufacturing enterprises need to quickly carry out dynamic scheduling management and allocate resources reasonably. Real-time performance index prediction results can provide a basis for production decision-making and evaluation, so it is an inevitable requirement to quickly understand the performance index achieved by the production line under a certain scheduling mode. [0003] At present, there are mainly three methods of predictive model modeling: mathematical modeling, simulation modeling, and machine learning mode...

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At present, there are no literatures and patents related to the above-mentioned production line performance prediction method

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