Organometallic photoresists are being pursued as an alternative photoresist material to push the current extreme ultraviolet lithography (EUVL) to the next generation of high-NA EUVL. In order to improve the photoresist performance, an understanding of the photoresist's response to different process conditions is required. In this endeavor, a stochastic development model is implemented, integrated into full photoresist process steps, and applied for photoresist performance investigations. The model is applied to Inpria-YA photoresist, which works mainly by the process of aggregation. Previously published modeling approaches for metal-organic photoresists assume that the development characteristics of these materials depend only on the size of the created oxo-clusters. In contrast to that, we propose a modeling approach that provides a more detailed description of the interaction among the developer, ligands, and oxo-bonds. Further, the calibration procedures conducted to extract the model parameters to match experimental data are discussed. The model approximated the experimental data with CD RMSE and LWR RMSE of 0.60 and 0.40 nm, respectively. We also investigated the impact of photoresist parameters on the process metrics, line width roughness (LWR), critical dimension (CD), doseto-size (DtS), and exposure latitude (EL) with the calibrated model. The investigation shows that details of the interaction of photoresist and developer, especially, the so-called development critical value, have a significant impact on the LWR and DtS. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Directed self-assembly (DSA) of block copolymers (BCP) is a promising alternative technology to overcome the limits of patterning for the semiconductor industry. DSA exploits the self-assembling property of BCPs for nano-scale manufacturing and to repair defects in patterns created during photolithography. After self-assembly of BCPs, to transfer the created pattern to the underlying substrate, selective etching of PMMA (poly (methyl methacrylate)) to PS (polystyrene) is required. However, the etch process to transfer the self-assemble "fingerprint" DSA patterns to the underlying layer is still a challenge. Using combined experimental and modelling studies increases understanding of plasma interaction with BCP materials during the etch process and supports the development of selective process that form well-defined patterns. In this paper, a simple model based on a generic surface model has been developed and an investigation to understand the etch behavior of PS-b-PMMA for Ar, and Ar/O 2 plasma chemistries has been conducted. The implemented model is calibrated for etch rates and etch profiles with literature data to extract parameters and conduct simulations. In order to understand the effect of the plasma on the block copolymers, first the etch model was calibrated for polystyrene (PS) and poly (methyl methacrylate) (PMMA) homopolymers. After calibration of the model with the homopolymers etch rate, a full Monte-Carlo simulation was conducted and simulation results are compared with the critical-dimension (CD) and selectivity of etch profile measurement. In addition, etch simulations for lamellae pattern have been demonstrated, using the implemented model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.