A Bayesian virtual metrology for quality inspection of mobile repeater systems

Received: 1 July 2016 Abstract Accepted: 28 July 2016 The technology of wideband code division multiple access (WCDMA) has been applied to band selective interference cancellation system (ICS) repeaters. To inspect the telecommunication quality of the systems, quality engineers must check the shape of the signals at the corresponding frequency band of the repeaters. However, measuring the signal quality is a repetitive manual task which requires much inspection time and high costs. In the case of small-sized samples, such as the example of an ICS repeater system, Bayesian approaches have been employed to improve the estimation accuracy by incorporating prior information on the parameters of the model in consideration. This research proposes a virtual method of quality inspection for products using a correlation structure of measurement data, mainly in a Bayesian regression framework. The Bayesian regression model derives prior information from historical measurement data to predict measurements of other frequency bandwidths by exploiting the correlation structure of each measurement data. Empirical results show the potential for reducing inspection costs and time by predicting the values of adjoining frequency bandwidths through measured data of a frequency bandwidth in the course of quality inspections of ICS repeater systems.

[1]  C.H. Yu,et al.  Virtual metrology: a solution for wafer to wafer advanced process control , 2005, ISSM 2005, IEEE International Symposium on Semiconductor Manufacturing, 2005..

[2]  Daniel B. Rowe,et al.  Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing , 2002 .

[3]  P. Kharchenko,et al.  Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.

[4]  P. Pérez-Rodríguez,et al.  Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction , 2015 .

[5]  Peter E. Rossi,et al.  Bayesian Statistics and Marketing , 2005 .

[6]  Young-Seok Kim,et al.  A Fundamental Study on Analysis of Electromotive Force and Updating of Vibration Power Generating Model on Subway Through The Bayesian Regression and Correlation Analysis , 2013 .

[7]  Peter D. Hoff,et al.  A First Course in Bayesian Statistical Methods , 2009 .

[8]  Paolo Braca,et al.  Bayesian Tracking in Underwater Wireless Sensor Networks With Port-Starboard Ambiguity , 2014, IEEE Transactions on Signal Processing.

[9]  C. Biller Adaptive Bayesian Regression Splines in Semiparametric Generalized Linear Models , 2000 .

[10]  James O. Berger Statistical Decision Theory , 1980 .

[11]  C. C. Kokonendji,et al.  A Bayesian Approach to Bandwidth Selection in Univariate Associate Kernel Estimation , 2013 .

[12]  Arnold Zellner,et al.  On the Bayesian Estimation of Multivariate Regression , 1964 .

[13]  D.M. Tilbury,et al.  An Approach for Factory-Wide Control Utilizing Virtual Metrology , 2007, IEEE Transactions on Semiconductor Manufacturing.