Gaussian process regression for virtual metrology of microchip quality and the resulting strategic sampling scheme

dc.contributor.advisorDjurdjanovic, Dragan
dc.creatorDarwin, Tyler Jackson
dc.creator.orcid0000-0002-1841-7096
dc.date.accessioned2017-09-19T18:26:11Z
dc.date.available2017-09-19T18:26:11Z
dc.date.created2017-08
dc.date.issued2017-09-15
dc.date.submittedAugust 2017
dc.date.updated2017-09-19T18:26:11Z
dc.description.abstractManufacturing of integrated circuits involves many sequential processes, often ex- ecuted to nanoscale tolerances, and the yield depends on the often unmeasured quality of intermediate steps. In the high-throughput industry of fabricating microelectronics on semi-conducting wafers, scheduling measurements of product quality before the electrical test of the complete IC can be expensive. We therefore seek to predict metrics of product quality based on sensor readings describing the environment within the relevant tool during the processing of each wafer, or to apply the concept of virtual metrology (VM) to monitor these intermediate steps. We model the data using Gaussian process regression (GPR), adapted to simultaneously learn the nonlinear dynamics that govern the quality characteristic, as well as their operating space, expressed by a linear embedding of the sensor traces’ features. Such Bayesian models predict a distribution for the target metric, such as a critical dimension, so one may assess the model’s credibility through its predictive uncertainty. Assuming measurements of the quality characteristic of interest are budgeted, we seek to hasten convergence of the GPR model to a credible form through an active sampling scheme, whereby the predictive uncertainty informs which wafer’s quality to measure next. We evaluate this convergence when predicting and updating online, as if in a factory, using a large dataset for plasma-enhanced chemical vapor deposition (PECVD), with measured thicknesses for ~32,000 wafers. By approximately optimizing the information extracted from this seemingly repetitive data describing a tightly controlled process, GPR achieves ~10% greater accuracy on average than a baseline linear model based on partial least squares (PLS). In a derivative study, we seek to discern the degree of drift in the process over the several months the data spans. We express this drift by how unusual the relevant features, as embedded by the GPR model, appear as the in- puts compensate for degrading conditions. This method detects the onset of consistently unusual behavior that extends to a bimodal thickness fault, anticipating its flagging by as much as two days.
dc.description.departmentMechanical Engineering
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T26970D5G
dc.identifier.urihttp://hdl.handle.net/2152/61673
dc.language.isoen
dc.subjectGaussian process regression
dc.subjectPlasma-enhanced chemical vapor deposition
dc.subjectActive learning
dc.subjectVirtual metrology
dc.subjectProcess drift
dc.titleGaussian process regression for virtual metrology of microchip quality and the resulting strategic sampling scheme
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentMechanical Engineering
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
DARWIN-THESIS-2017.pdf
Size:
1.67 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.45 KB
Format:
Plain Text
Description:
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.84 KB
Format:
Plain Text
Description: