Semiconductor process monitoring method based on independent component analysis and Bayesian inference

Abstract

The invention discloses a semiconductor process monitoring method based on independent component analysis and Bayesian inference, comprising the following step of: firstly, dividing working conditions according to the mixed data of the semiconductor process, conducting the independent component analysis for each working condition data, and establishing a corresponding independent component analysis model; and then integrating and combining the monitoring information under the different working conditions by a Bayesian inference method to obtain a final monitoring result. In addition, the invention can also acquire the working condition information of current monitoring data by a posterior probability analysis method, that is to say, the invention can judge that the current monitoring data is in what process operation working condition; compared with the present other methods, the invention can not only greatly enhance the monitoring effect of the semiconductor process, but also largely improve the dependence of the monitoring method on process knowledge and enhance the comprehensive ability and the operating confidence of process operators on the process, thereby being more beneficial to the implementation of the automation of the semiconductor process.

Description

technical field [0001] The invention belongs to the field of semiconductor industrial process control, in particular to a process monitoring method based on independent component analysis and Bayesian reasoning. Background technique [0002] In recent years, the monitoring problem of the semiconductor industrial production process has been paid more and more attention by the industry and academia. On the one hand, since the semiconductor industry process itself has extremely high requirements on product quality, how to effectively prevent the process from producing inferior or unqualified products is an urgent problem to be solved. On the other hand, if the process is not well monitored, operational accidents may occur, which may affect the quality of the product in the light, and cause loss of life and property in the severe case. In addition, the results obtained from monitoring the semiconductor process can in turn guide the improvement of the production process and prod...

AI Technical Summary

Problems solved by technology

Traditional monitoring methods assume that the process operates under a single working condition, which can no longer meet the monitoring requirements of semiconductor processes

Even if the different operating conditions of the process are modeled separately, satisfactory monitoring results cannot be achieved

Because when monitoring new process data, it is necessary to combine process knowledge to judge the operating conditions of the data and select the corresponding monitoring model, which greatly enhances the dependence of monitoring methods on process knowledge, which is not conducive to the development of semiconductor processes. automated implementation

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