Wafer yield prediction method based on deep learning model

Abstract

The invention relates to a wafer yield prediction method based on a deep learning model. The method comprises the following steps: carrying out data preprocessing on electrical test parameters in theactual production process of a wafer, and processing redundant data in the electrical test data of the wafer in combination with a principal component analysis method to obtain low-correlation data suitable for model input; secondly, dividing a training set and a test set for input key electrical test parameters; designing a continuous deep belief network model for predicting the wafer yield on the basis. The model mainly comprises two parts, the first part is a multi-hidden-layer continuous restricted Boltzmann machine model and is used for extracting feature information of input variables ofthe model, and the second part is an error back propagation network model and is used for finely adjusting the extracted feature error information. According to the invention, the wafer yield can beaccurately predicted by using the electrical test data in the wafer production process.

Description

technical field [0001] The invention relates to the technical field of semiconductor wafer yield prediction technology, in particular to a wafer yield prediction method based on a deep learning model. Background technique [0002] my country's integrated circuit industry has developed rapidly, and now it has formed a trend of common development of product design, chip manufacturing, and circuit packaging. Due to the large investment in the production of integrated circuit products and the high cost of losses, predicting the yield of wafer production in advance is of great significance for improving the wafer production process, reducing wafer production losses, and controlling chip production costs. [0003] The traditional wafer yield prediction model mainly considers the relationship between the source of defects, the number of defects, the degree of defect aggregation and the yield of the wafer, and these yield prediction models require comprehensive statistical analysis ...

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Problems solved by technology

And the yield prediction model based on neural network is mainly used, and the neural network model has a long learning process when dealing with high-dimensional data, the gradient is easy to disappear, and it is easy to fall into local optimum, making the model affected by input noise Large, resulting in model instability, it is difficult to effectively deal with the complex nonlinear relationship between wafer electrical test parameters and yield, and obtain higher prediction accuracy

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