journal article·2024-12-10

Area-Selective Molecular Layer Deposition of Alucone on Photoresist for Enhanced Pattern Transfer

Long Liu, Yue Zhang, Wenda Bao, Weixuan Zeng, Xiaoyun Yu, Jiayi Chen, Shisheng Xiong

Chemistry of Materials· Volume 36· Issue 23
Publisher page

Abstract

Multilayer mask dry etching is commonly used for pattern transfer on industrial lines to reduce the semiconductor component size. One of the challenges is the need for improved photoresists (PRs) that combine high resolution and etch selectivity with low complexity and material cost. Here, an alternative masking approach using area-selective molecular layer deposition (AS-MLD) has been shown to improve the etch resistance and pattern transfer accuracy. This technology enables the selective deposition of alucone into prepatterned PR areas or a class of PRs rich in ester groups like poly(methyl methacrylate) (PMMA), avoiding the deposition on the silicon-based substrate. The AS-MLD minimizes feature size variation and reduces edge placement errors. In this work, the mechanism of selective deposition of alucone on PMMA and the inhibition mechanism on Si-based substrates were investigated in detail. The alucone-PMMA demonstrates considerable resistance to high-energy fluorine plasma etching. Compared to the original PMMA, the etch resistance is improved by 85 times and the etch selectivity between alucone-PMMA-trimmed and SiON or SiO 2 can reach as high as 52 or 32, respectively. By improving film uniformity and thickness control, this method simplifies the manufacturing process, increases production efficiency, and reduces costs. Our approach addresses the urgent need for improved deposition selectivity and resolution in extreme ultraviolet (EUV) lithography and is an important step toward achieving reliable and high-performance semiconductor devices.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Semiconductor materials and devices · Thin-Film Transistor Technologies · ZnO doping and properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Extreme Technology Institute, Zhangjiang Labotory, Shanghai 201210, China

School of Information Science and Technology, Fudan University, Shanghai 200433, China

Funding

National Natural Science Foundation of China

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