Semiconductor Manufacturing Process Monitoring Using Gaussian Mixture Model and Bayesian Method With Local and Nonlocal Information

2012 ◽  
Vol 25 (3) ◽  
pp. 480-493 ◽  
Author(s):  
Jianbo Yu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Semiconductor Manufacturing ◽  
Bayesian Method ◽  
Gaussian Mixture

A multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections

2021 ◽  
pp. 014233122110605
Author(s):  
Qingxiu Guo ◽  
Jianchang Liu ◽  
Shubin Tan ◽  
Dongsheng Yang ◽  
Yuan Li ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Process Monitoring ◽  
Feature Space ◽  
Gaussian Mixture ◽  
Variational Inference ◽  
Monitoring Strategy ◽  
Locality Preserving Projections ◽  
Model Based ◽  
Locality Preserving

For multimode process monitoring, accurate mode information is difficult to be obtained, and each mode is monitored separately, which increases the complexity of the system. This paper proposes a multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections (IVIGMM-LPP). First, the raw data are projected to the feature space where samples still maintain the original neighbor structure. Second, a new discriminant condition is introduced to reduce the influence of the initial category parameter on the iteration results in the VIGMM model. Then, the data are updated utilizing modal information, so that the scales of different modes are adjusted to the same level. Next, the deviation vector is introduced to eliminate the multi-center structure of data. Finally, the statistic is built to monitor the process. IVIGMM-LPP establishes one model for monitoring the premise of knowing the mode information, which reduces the complexity of the monitoring process and improves the fault detection rate. The experimental results of a numerical case and the Tennessee Eastman (TE) process verify the effectiveness of IVIGMM-LPP.

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