Leveraging data analytics, patterning simulations and metrology models to enhance CD metrology accuracy for advanced IC nodes

Abstract

Integrated Circuit (IC) technology is changing in multiple ways: 193i to EUV exposure, planar to non-planar device architecture, from single exposure lithography to multiple exposure and DSA patterning etc. Critical dimension (CD) control requirement is becoming stringent and more exhaustive: CD and process window are shrinking., three sigma CD control of < 2 nm is required in complex geometries, and metrology uncertainty of < 0.2 nm is required to achieve the target CD control for advanced IC nodes (e.g. 14 nm, 10 nm and 7 nm nodes). There are fundamental capability and accuracy limits in all the metrology techniques that are detrimental to the success of advanced IC nodes. Reference or physical CD metrology is provided by CD-AFM, and TEM while workhorse metrology is provided by CD-SEM, Scatterometry, Model Based Infrared Reflectrometry (MBIR). Precision alone is not sufficient moving forward. No single technique is sufficient to ensure the required accuracy of patterning. The accuracy of CD-AFM is ~1 nm and precision in TEM is poor due to limited statistics. CD-SEM, scatterometry and MBIR need to be calibrated by reference measurements for ensuring the accuracy of patterned CDs and patterning models. There is a dire need of measurement with < 0.5 nm accuracy and the industry currently does not have that capability with inline measurments. Being aware of the capability gaps for various metrology techniques, we have employed data processing techniques and predictive data analytics, along with patterning simulation and metrology models, and data integration techniques to selected applications demonstrating the potential solution and practicality of such an approach to enhance CD metrology accuracy. Data from multiple metrology techniques has been analyzed in multiple ways to extract information with associated uncertainties and integrated to extract the useful and more accurate CD and profile information of the structures. This paper presents the optimization of scatterometry and MBIR model calibration and feasibility to extrapolate not only in design and process space but from one process step to a previous process step. Well calibrated scatterometry model or patterning simulation model can be used to accurately extrapolate and interpolate in the design and process space for lithography patterning where AFM is not capable to accurately measure sub-40 nm trenches. Uncertainty associated with extrapolation can be large and needs to be minimized. We have made use of measurements from CD-SEM and CD-AFM, along with the patterning and scatterometry simulation models to estimate the uncertainty associated with extrapolation and methods to reduce it. First time we have reported the application of machine learning (Artificial Neural Networks) to the resist shrinkage systematic phenomenon to accurately predict the preshrink CD based on supervised learning using the CD-AFM data. The study lays out various basic concepts, approaches and protocols of multiple source data processing and integration for hybrid metrology approach. Impacts of this study include more accurate metrology, patterning models and better process controls for advanced IC nodes. © 2014 SPIE.