Gaussian Process Regression for Virtual Metrology of Microchip Quality and the Resulting Selective Sampling Scheme

Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Manufacturing of integrated circuits involves many sequential processes executed to nanoscale tolerances, and the yield depends on the often-unmeasured quality of intermediate steps. Taking physical quality measurements in this high-throughput industry can be expensive and time-consuming. Instead, we seek to predict product quality characteristics using readily available sensor readings of the tool environment during processing of each wafer and employ Gaussian Process Regression (GPR) paradigm to realize this Virtual Metrology (VM) concept. Convergence of the GPR based VM estimation of product quality is hastened through an active sampling scheme, whereby the predictive uncertainty of the GPR model informs which wafer’s quality to measure next in order to obtain maximal additional information for the VM model. We evaluate these methods using a large dataset collected from a plasma-enhanced chemical vapor deposition (PECVD) process, with relevant tool sensor readings and the corresponding physical measurements of mean film thicknesses for 32,000 wafers. By selecting which wafers to physically measure for VM model updates, the GPR based VM method achieves ~10% greater accuracy on average than the partial least squares based method.

Keywords

Virtual metrology Gaussian process regression Plasma-enhanced chemical vapor deposition Semiconductor manufacturing Virtual metrology Process drift 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Tyler Darwin
    • 1
  • Roman Garnett
    • 2
  • Dragan Djurdjanovic
    • 1
  1. 1.Department of Mechanical EngineeringUniversity of Texas at AustinAustinUSA
  2. 2.Department of Computer Science and EngineeringWashington University in St. LouisSt. LouisUSA

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