A chemical mechanical grinding time setting method based on clustering and multi-task learning

Abstract

The chemical mechanical grinding time setting method based on clustering and multi-task learning belongs to the field of automatic control, information technology and advanced manufacturing. The method takes the process index and product state as input, and the key factor affecting the process index - grinding time is used as the output to establish a reverse model to optimize the setting of grinding time. In the process of constructing the above reverse model, in view of the problem of many types of production varieties and little data of a single variety, similar varieties are clustered according to product characteristics, and a multi-task learning method based on common parameter extraction is used for modeling in each category; The calculated model parameters are divided into the common part of this kind of variety and the private part of single variety.

Description

technical field [0001] The invention belongs to the fields of automatic control, information technology and advanced manufacturing. In order to solve the problems of high rework rate and low production efficiency of the entire production line caused by manual setting of chemical mechanical grinding time in the multi-variety mixed production mode in modern microelectronics production and manufacturing, the present invention adopts an optimal setting method based on a reverse model , taking the process index and product status as input, and the key factor affecting the process index—grinding time as output—to establish a reverse model to optimize the setting of grinding time. Aiming at the problem that there are many product varieties in the production data and the data volume of a single variety is small, a chemical mechanical grinding time setting method based on clustering and multi-task learning is proposed, which can realize the optimal setting of chemical mechanical grindi...

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Problems solved by technology

[0003] In order to solve the problem of optimal setting of chemical mechanical grinding time in a multi-variety mixed production environment, the present invention takes process index and product status as input, and the key factor affecting process index-grinding time as output to establish a reverse model for optimizing the grinding time Based on this, a chemical mechanical grinding time setting method based on clustering and multi-task learning is proposed

In order to deal with the problem of many varieties of products and little data of each variety in the actual production data, a two-step modeling method based on clustering and multi-task learning is proposed, which integrates the clustering process and parameter learning in traditional clustering multi-task learning Process considered separately

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