Photo-etching line width intelligence forecasting method based on dimension-reduction and quantity-increment-type extreme learning machine

Abstract

The invention provides a photo-etching line width intelligence forecasting method based on a dimension-reduction and quantity-increment-type extreme learning machine, belongs to the field of automatic control, information technology and advanced manufacturing, and particularly relates to a method that special to the characteristic that training data are high in dimensionality and arrives in a batching mode in the modeling process of a photo-etching line width index, intelligent online forecasting of the photo-etching line width index is achieved by conducting matrix inversion and dimension reduction to a batching extreme learning machine based on minimizing of structural risks. The photo-etching line width intelligence forecasting method based on the dimension-reduction and quantity-increment-type extreme learning machine is characterized by comprising the following steps: matrix inversion in the batching extreme learning machine based on the minimizing of structural risks is conducted dimension reduction by adopting a matrix inversion dimension reduction formula to build the relations between model parameters of the extreme learning machine and new-arriving data, and online increment-quantity-type leaning and updating of output layer weights of the model parameters of the extreme learning machine are achieved. The index forecasting method which is used for the dimension-reduction and quantity-increment-type extreme learning machine and based on matrix inversion dimension reduction has good forecasting effect.

Description

technical field [0001] The invention belongs to the fields of automatic control, information technology and advanced manufacturing. Aiming at the characteristics of high data dimensionality and batch arrival of training data in the process of predicting lithography line width, an online augmentation method based on matrix inversion and dimensionality reduction is proposed. The intelligent prediction method of the lithography line width of the quantitative extreme learning machine can realize the online adjustment of the parameters of the index prediction model, and the method has better prediction accuracy and efficiency. Background technique [0002] Lithographic line width is a key process index that affects the yield of microelectronics products, but at present there is a large lag in its detection results, and it is difficult to achieve online optimization and adjustment of relevant key process operating parameters that affect this index, thus affecting the product yield ...

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Problems solved by technology

However, since the above methods are based on empirical risk minimization criteria, there are defects such as overfitting

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