Outlier Detection to Assure Good Quality for Metrology Bias Acceptance Test
The quality of measurement system in semiconductor manufacturing industry has been inevitably important to assure good quality of IC products being manufactured. Metrology tools bias acceptance test is a regular test to decide whether a tool needs re-calibration. The current practice in manufacturing industries follows the renowned Measurement Systems Analysis (MSA) Reference Manual by Chrysler, Ford, and General Motors. However, most practitioners ignore outlier detection on the repeated measurement data, largely due to the fact that the MSA Reference Manual does not cover the outlier detection for bias acceptance test although ISO 5725 requires the outlier detection and replacement with correct values. This article discusses the impact of the outliers if existed on the bias acceptance test and the statistical methods of outlier detection. If the outliers are not detected and replaced with correct values, the bias acceptance test could fail to reject a metrology tool with unacceptable bias. This article introduces the statistical definition of the outlier, basics of statistical criteria for outliers and possible causes for the existence of outliers. Additional methods to detect outliers besides those recommended by ISO 5725 are introduced to assure good quality for metrology bias acceptance tests.
S.F. Yang W.T.K. Chien
Corporate Q & R Center, Semiconductor Manufacturing International Corporation, Shanghai 201203, China
国际会议
China Semiconductor Technology International Conference 2010(中国国际半导体技术大会 CSTIC)
上海
英文
145-150
2010-03-18（万方平台首次上网日期，不代表论文的发表时间）