Increasing detectability in semiconductor foundry by multivariate statistical process control

Abstract

Quality has become a key determinant of success in all aspects of modern industries. It is especially prominent in the semiconductor industry. This paper reviews the contributions of statistical analysis and methods to modern quality control and improvement. The two main areas are statistical process control (SPC) and experimentation. The statistical approach is placed in the context of recent developments in quality management, with particular reference to the total quality movement.

In SPC, Hotelling T² has been applied in laboratories with good result; however, it is rarely used in mass production, especially in the semiconductor industry. An advance process control (APC) of R&D study, involving Hotelling T² and principal component analysis (PCA), is performed on a high density plasma chemical vapour deposition (HDP CVD) equipment in the 12-inch wafer fab. The design of experiment (DOE) of gas flow and RF power effects is used to work the feasibility of PCA for SPC and examine the correlation among tool parameters. In this work, the Hotelling T² model is shown to be sensitive to variations as small as (+/ − )5% in the tool parameters. Compared with classical PDCA and qualitative analysis, applying statistical in process control is more effective and indeed necessary. This model also is especially suitable to the semiconductor industry.

Keywords:

• statistical process control (SPC)
• advance process control (APC)
• fault detection and classification (FDC)
• Hotelling T²
• principal component analysis (PCA)
• semiconductor industry

Acknowledgements

The work was supported by Taiwan Semiconductor Manufacturing Company. For the reason of protection of proprietary information, only portions of actual execution results were disclosed. We would like to thank the officers and employees for their valuable suggestions and support.