Theoretical Insights into the Solubility Polarity Switch of Metal-Organic Nanoclusters for Nanoscale Patterning

Polymerizable Nonionic Perfluorinated Photoacid Generators for High-Resolution Lithography

Advanced photoresists must satisfy stringent sensitivity requirements while maintaining the ability to print ever-shrinking critical dimensions. However, the unavoidable acid diffusion associated with chemically amplified photoresists has led to a trade-off between resolution, line-edge roughness, and sensitivity, which presents a significant challenge for high-resolution lithography. To address this issue, a novel class of alkene-functionalized nonionic perfluorinated photoacid generators (PAGs) is developed. These fluorine-rich compounds significantly enhance the photochemical reactivity due to the introduction of abundant F-elements, thereby improving sensitivity. Upon irradiation by ultraviolet light or electrons, they generate long-chain perfluorinated sulfonic acids with large sizes and minimal diffusion ranges, effectively suppressing acid diffusion. Furthermore, by employing these polymerizable PAG monomers, PAG-bound polymers are synthesized that are likely to achieve higher resolution by minimizing acid diffusion. Lithography performance demonstrated patterning of sub-45 nm lines at an electron beam dose of 29 µC cm-2. Overall, the developed perfluorinated PAGs and PAG-bound polymer photoresists are promising candidates for achieving high-sensitivity and high-resolution nano-patterning.

Synthesis of pentameric chlorotin carboxylate clusters for high resolution EUV photoresists under small doses

This work reports the synthesis and characterization of a novel pentameric tin chloro cluster, (vinylSn)3Sn2Cl5O2(OH)2(t-BuCO2)6 (1), and explores its application as an efficient negative-tone photoresist in a 1 : 2 weight ratio blend with [(n-BuSn)12O14(OH)6](BF4)2 (2). Through e-beam lithography, a small high-resolution pattern (HP = 20 nm) is achieved for the blend photoresist (3) at a dose of 2080 μC cm-2. Additionally, EUV lithography demonstrates the development of a high-resolution pattern (HP = 16 nm) at an EUV dose of 70 mJ cm-2. Mechanistic studies by reflective FTIR indicate a significant decomposition of Sn-carbon and SnO2(t-Bu) moieties starting at J = 35 mJ cm-2, which is accompanied by growth of the Sn-O absorption intensity. A collapse of the cluster frameworks of clusters (1) and (2) is observed at J > 70 mJ cm-2. High-resolution X-ray photoelectron spectroscopy (HRXPS) reveals that low EUV light predominantly decomposes Sn-butyl and Sn-Cl bonds. As EUV doses increase, primary photolytic reactions involve cleavage of Sn-butyl, Sn-O2CBut, and Sn-vinyl bonds. Notably, the photolytic decomposition of Sn-Cl bonds is distinctive, with only two out of five bonds being cleaved, even at high EUV doses, resulting in a break in film growth at J = 27-35 mJ cm-2 in the EUV contrast curve. Moreover, HRXPS analysis suggests that radical propagation on the vinyltin end of the blend is unlikely, providing concise mechanistic insights into the photochemical processes governing the behavior of this advanced photoresist.

Machine learning in electron beam lithography to boost photoresist formulation design for high-resolution patterning

The reduction of the critical dimension (CD) usually improves the resolution of patterns and performance of chips. In chip manufacturing, electron beam lithography (EBL) is a promising technology for preparing sub-10 nm patterns, and its imaging resolution is primarily determined by the photoresist formulation. However, the smaller CDs are mainly achieved by optimizing process conditions, and little attention has been paid to the photoresist formulation optimization. Screening suitable photoresist formulations remains a significant challenge due to the considerable time and high cost. Herein, we report a formulation optimization technique of a metal oxide nanoparticle photoresist that combines EBL experiments with a machine learning long short-term memory (LSTM) network. Using the LSTM network, a CD photoresist evaluation model is established. Leveraging the CD model, a photoresist formulation optimizer is developed with a line width of 26 nm. The verification results demonstrate that the CDs predicted by the LSTM network are basically consistent with the EBL experimental results, and the photoresist formulations that meet the CD requirements can be screened. This work opens up a novel perspective to boost photoresist formulation design for high-resolution patterning with artificial intelligence and provides guidance for EBL experiments.