For the past years, ArF immersion has been employed as the major lithography tool in the foundry manufacturing to fabricate the patterns of minimum pitch and size. However, for semiconductor scaling beyond N7 the application of EUV lithography is considered to be crucially important to overcome the physical limitation of ArF immersion and to realize even smaller patterns. In the case of ArF photo processes, the best mask size for a specific pitch could be selected with the consideration of optical performances such as NILS, MEEF, etc. In contrast, for the EUV processes the optical and resist stochastic effect should also be taken into account as an important factor in deciding the best mask size. In this paper, we are going to discuss the dose and mask size optimization process for an DRAM contact hole layer with EUV lithography utilizing stochastic simulations; this contains also the stochastic response of the resist. In order to calibrate a predictive stochastic resist model, which is required for this application, measurements of the stochastic resist response are necessary. In addition, the systematic and stochastic errors of CD-SEM measurements have to be estimated. We will compare experimentally obtained NILS and MEEF to simulated results, which are in very good agreement. Also, we show a comparison of experimental and computational analysis of LCDU (Local CD Uniformity).
Finding a process window and improving the yield for EUV single exposure nodes requires an understanding of stochastic defects. Stochastic simulations can be used as a tool to understand the influence of the process on defectivity. This presentation introduces stochastic model calibration with the purpose of matching lithographic observables. The validity of this approach will be shown based on comparison of measured defectivity data and matching stochastic simulations.
Proc. SPIE. 11323, Extreme Ultraviolet (EUV) Lithography XI
KEYWORDS: Lithography, Data modeling, Calibration, Photoresist materials, Monte Carlo methods, Line width roughness, Extreme ultraviolet lithography, Photoresist processing, Stochastic processes, Systems modeling
MOx resists have matured into promising alternatives to conventional CAR resists for advanced-node EUV lithography where these materials offer potential improvements to patterning fidelity and high etch resistance based on metallic components. This is a particular boon for processes with limited exposure latitudes such as High-NA EUV lithography. Creating and employing first-principle models of MOx lithographic processes should speed adoption and development of these materials and represents an important aspect of platform maturation. Stochastic photochemical models of metalcontaining resist systems have previously been developed, but without extension to computational lithography. Likewise, stochastic models derived from CAR systems have been fit to MOx lithographic data using computational lithography software, facilitating limited stochastic lithography studies without capturing fundamental MOx imaging processes. Recently, a rigorous stochastic model built from the ground up using MOx-specific resist principles has been developed. In this contribution, the performance of this MOx-specific model was assessed by comparing simulated and experimental lithography data for a series of MOx resists under a range of exposure and process conditions. Chemical and physical properties of the resists derived independently from X-ray diffraction, EUV absorbance, FTIR spectroscopy, and ellipsometry measurements were parameterized in the context of the simulation, and calibration routines were used to fit simulated data to experimental CD-SEM exposure data produced using an NXE-3300B EUV scanner. Insights from these models may be used to guide MOx resist development and EUV lithography process optimization. Ultimately, these studies will help to identify process windows, processing points, and possibly improvements to the MOx resists.
Stochastic defects are a concern in the lithographic processes used for semiconductor manufacturing, particularly for advanced node extreme ultraviolet lithographic processes. Experimentally determining the defect probability for a lithographic process is extremely time-consuming, requiring expensive metrology equipment and generally limited to simple patterns. Defect probability simulations can be beneficial from time and cost perspective and furthermore should be extensible to more complex patterns. As such, being able to accurately predict the defect probability using lithography simulations would be a valuable complementary option. We show the results of a fast simulation-based methodology for predicting defect probabilities based on a continuum lithographic model calibrated to experimental data. The simulation based-results are compared to experimental microbridge defect probability data where we show a good correlation between the two.
Mask absorber variations are known to impact wafer imaging. To understand these impacts, absorber variations around SRAF and line-end features are studied on both bright and dark field masks. The primary areas of investigation are SRAF absorber thickness and sidewall angle variation. The working hypothesis was that these two variations are most prevalent in EUV mask absorber processing and could limit EUV imaging. In addition, this study will investigate whether Optical Proximity Correction (OPC) and can compensate for absorber thickness and sidewall variations. AFM data were collected to identify whether qualitative variations between SRAF and main features in the mask absorber were present. Simulations were deployed to quantify the response of wafer images to mask absorber variations. The study found sensitivity to SRAF SWA and thickness variations in the dark field and bright field cases. The study also found that OPC mitigates a large part of the mask SRAF shape variations, if the OPC model includes the quantified variation. Consequently, mask characterization and inclusion in OPC models is needed to reduce model errors.
This paper presents a design and technology co-optimization (DTCO) study of metal cut formation in the sub-20-nmregime. We propose to form the cuts by applying grapho-epitaxial directed self-assembly. The construction of a DTCO flow is explained and results of a process variation analysis are presented. We examined two different DSA models and evaluated their performance and speed tradeoff. The applicability of each model type in DTCO is discussed and categorized.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print format on
SPIE.org.