Using qualitative observations for process tuning and control [ICmanufacture]
Spanos, C.J.
Chen, R.L.
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA;
This paper appears in: Semiconductor Manufacturing, IEEE Transactions on
Publication Date: May 1997
Volume: 10,
Issue: 2
On page(s): 307-316
ISSN: 0894-6507
References Cited: 16
CODEN: ITSMED
INSPEC Accession Number: 5575598
Digital Object Identifier: 10.1109/66.572086
Current Version Published: 2002-08-06
Abstract
Many qualitative properties of the product and the process are of
interest during semiconductor manufacturing. One of the typical examples
is the sidewall surface roughness of an etched polysilicon line. These
properties are important since they affect directly the quality and
performance of the integrated circuit (IC) devices being built.
Traditionally, however, they are treated informally and subjectively as
tacit knowledge in the processing arena. In this paper, we present a
systematic approach to modeling and controlling such qualitative
properties. This approach is based on treating qualitative process
variables as categorical data that can be better understood with the
help of formal statistical analysis known as logistic regression. This
analysis reveals important relationships between the input process
settings and the qualitative process output responses in a way that is
similar to linear regression analysis for conventional numerical
variables. Similarly, categorical process variables can be used for
process control, which is driven by a probabilistic model of the
categorical variables. We show how categorical models can be used to
tune a process and, later, to control it via statistical process control
(SPC) charts, model-based quality control techniques, and adaptive
run-by-run controllers
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