Share Email Print

Proceedings Paper

Parameter extraction with neural networks
Author(s): Luca Cazzanti; Mumit Khan; Franco Cerrina
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In semiconductor processing, the modeling of the process is becoming more and more important. While the ultimate goal is that of developing a set of tools for designing a complete process (Technology CAD), it is also necessary to have modules to simulate the various technologies and, in particular, to optimize specific steps. This need is particularly acute in lithography, where the continuous decrease in CD forces the technologies to operate near their limits. In the development of a 'model' for a physical process, we face several levels of challenges. First, it is necessary to develop a 'physical model,' i.e. a rational description of the process itself on the basis of know physical laws. Second, we need an 'algorithmic model' to represent in a virtual environment the behavior of the 'physical model.' After a 'complete' model has been developed and verified, it becomes possible to do performance analysis. In many cases the input parameters are poorly known or not accessible directly to experiment. It would be extremely useful to obtain the values of these 'hidden' parameters from experimental results by comparing model to data. This is particularly severe, because the complexity and costs associated with semiconductor processing make a simple 'trial-and-error' approach infeasible and cost- inefficient. Even when computer models of the process already exists, obtaining data through simulations may be time consuming. Neural networks (NN) are powerful computational tools to predict the behavior of a system from an existing data set. They are able to adaptively 'learn' input/output mappings and to act as universal function approximators. In this paper we use artificial neural networks to build a mapping from the input parameters of the process to output parameters which are indicative of the performance of the process. Once the NN has been 'trained,' it is also possible to observe the process 'in reverse,' and to extract the values of the inputs which yield outputs with desired characteristics. Using this method, we can extract optimum values for the parameters and determine the process latitude very quickly.

Paper Details

Date Published: 8 June 1998
PDF: 11 pages
Proc. SPIE 3332, Metrology, Inspection, and Process Control for Microlithography XII, (8 June 1998); doi: 10.1117/12.308780
Show Author Affiliations
Luca Cazzanti, Univ. of Wisconsin/Madison (United States)
Mumit Khan, Univ. of Wisconsin/Madison (United States)
Franco Cerrina, Univ. of Wisconsin/Madison (United States)

Published in SPIE Proceedings Vol. 3332:
Metrology, Inspection, and Process Control for Microlithography XII
Bhanwar Singh, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?