Share Email Print
cover

Proceedings Paper

Splendidly blended: a machine learning set up for CDU control
Author(s): Clemens Utzny
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

As the concepts of machine learning and artificial intelligence continue to grow in importance in the context of internet related applications it is still in its infancy when it comes to process control within the semiconductor industry. Especially the branch of mask manufacturing presents a challenge to the concepts of machine learning since the business process intrinsically induces pronounced product variability on the background of small plate numbers. In this paper we present the architectural set up of a machine learning algorithm which successfully deals with the demands and pitfalls of mask manufacturing. A detailed motivation of this basic set up followed by an analysis of its statistical properties is given. The machine learning set up for mask manufacturing involves two learning steps: an initial step which identifies and classifies the basic global CD patterns of a process. These results form the basis for the extraction of an optimized training set via balanced sampling. A second learning step uses this training set to obtain the local as well as global CD relationships induced by the manufacturing process. Using two production motivated examples we show how this approach is flexible and powerful enough to deal with the exacting demands of mask manufacturing. In one example we show how dedicated covariates can be used in conjunction with increased spatial resolution of the CD map model in order to deal with pathological CD effects at the mask boundary. The other example shows how the model set up enables strategies for dealing tool specific CD signature differences. In this case the balanced sampling enables a process control scheme which allows usage of the full tool park within the specified tight tolerance budget. Overall, this paper shows that the current rapid developments off the machine learning algorithms can be successfully used within the context of semiconductor manufacturing.

Paper Details

Date Published: 28 September 2017
PDF: 9 pages
Proc. SPIE 10446, 33rd European Mask and Lithography Conference, 104460N (28 September 2017); doi: 10.1117/12.2279430
Show Author Affiliations
Clemens Utzny, Advanced Mask Technology Ctr. GmbH & Co. KG (Germany)


Published in SPIE Proceedings Vol. 10446:
33rd European Mask and Lithography Conference
Uwe F.W. Behringer; Jo Finders, Editor(s)

© SPIE. Terms of Use
Back to Top