Airborne molecular contaminants (AMCs) in the cleanroom have been known to impact the performance of photoresist materials. Here, we investigate the effect of the environment composition during post-exposure delay (PED) on metal oxide resists (MOR). We applied specific environments during a fixed PED time and measured the impact of each condition on the printed L/S patterns by measuring the change in the final critical dimension (CD) in each case compared to a reference control. We performed chemical analysis to elucidate which chemical changes take place in the resist under the conditions that induced the largest CD changes. Our results suggest that the magnitude of the CD change after a fixed PED time depends on two main variables: the concentration of contaminants and the humidity of the environment. The chemical analysis after a PED in the presence of contaminants and humidity revealed that extra ligand cleavage takes place under these conditions compared to the reference non-delayed conditions. The results show that deviations from the target CD in MOR can be prevented by having control over humidity, contaminant levels, and PED time during processing. Furthermore, new optimized formulations designed to hinder extra chemical reactions during PED show a stable CD even when the PED was applied in environments with humidity and contaminants.
KEYWORDS: Data modeling, Calibration, Lithography, Monte Carlo methods, Stochastic processes, Extreme ultraviolet lithography, Photoresist processing, Line width roughness, Systems modeling, Photoresist materials
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.
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