Akaike information criterion to select well-fit resist models

Andrew Burbine; David Fryer; John Sturtevant

doi:10.1117/12.2085770

18 March 2015 Akaike information criterion to select well-fit resist models

Andrew Burbine, David Fryer, John Sturtevant

Proceedings Volume 9427, Design-Process-Technology Co-optimization for Manufacturability IX; 94270J (2015) https://doi.org/10.1117/12.2085770
Event: SPIE Advanced Lithography, 2015, San Jose, California, United States

Abstract

In the field of model design and selection, there is always a risk that a model is over-fit to the data used to train the model. A model is well suited when it describes the physical system and not the stochastic behavior of the particular data collected. K-fold cross validation is a method to check this potential over-fitting to the data by calibrating with k-number of folds in the data, typically between 4 and 10. Model training is a computationally expensive operation, however, and given a wide choice of candidate models, calibrating each one repeatedly becomes prohibitively time consuming. Akaike information criterion (AIC) is an information-theoretic approach to model selection based on the maximized log-likelihood for a given model that only needs a single calibration per model. It is used in this study to demonstrate model ranking and selection among compact resist modelforms that have various numbers and types of terms to describe photoresist behavior. It is shown that there is a good correspondence of AIC to K-fold cross validation in selecting the best modelform, and it is further shown that over-fitting is, in most cases, not indicated. In modelforms with more than 40 fitting parameters, the size of the calibration data set benefits from additional parameters, statistically validating the model complexity.

Citation Download Citation

Andrew Burbine, David Fryer, and John Sturtevant "Akaike information criterion to select well-fit resist models", Proc. SPIE 9427, Design-Process-Technology Co-optimization for Manufacturability IX, 94270J (18 March 2015); https://doi.org/10.1117/12.2085770

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available