Performance prediction method applicable to dynamic scheduling for semiconductor production line

Abstract

The invention discloses a performance prediction method applicable to dynamic scheduling for a semiconductor production line. An extreme learning machine (ELM) is applied to prediction and modeling in the performance prediction method. Feeding control and scheduling rules are considered in a unified manner in the method, short-term scheduling key performance indexes such as an equipment utilization rate and a movement step number are predicted on the basis of a real-time state of a system, and a foundation is provided for dynamic real-time scheduling. A novel feed-forward neural network of the ELM is introduced into the semiconductor manufacturing system, and a prediction model is built by the aid of available data of the production line. As shown by test results, ideal prediction results can be quickly acquired by the method implemented by the aid of the ELM, the method has obvious advantages and an obvious application prospect in the aspects of parameter selection and learning speed as compared with the traditional neural network modeling method, and a new idea is provided for online optimal control.

Description

Technical field [0001] The invention belongs to the field of semiconductor manufacturing, in particular to a semiconductor production line scheduling system based on an extreme learning machine. Background technique [0002] The semiconductor wafer production line is recognized as one of the most complex production lines. It has the characteristics of multiple equipment, multiple product types, reentrance, scrap and reprocessing of semi-finished products, and machine failure, so it brings a great deal to the scheduling and control of the production line. Complexity. [0003] A complete semiconductor production line scheduling includes feeding control and workpiece scheduling. Feeding control is used to determine the rate at which materials enter the production system. Workpiece scheduling refers to the use of a piece of equipment that competes with each other. When the equipment is idle, what decision should be made to select the next piece to be processed. Because the predictive...

AI Technical Summary

Problems solved by technology

However, the learning speed of the traditional neural network cannot meet the requirements of high real-time performance and the need to respond quickly and effectively to dynamic uncertain factors in the production site, which has become an important bottleneck in its application, especially for real-time online prediction.

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