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1. Self-Collapse Lithography

   https://doi.org/10.1021/acs.nanolett.7b02269  

   Zhao, Chuanzhen; Xu, Xiaobin; Yang, Qing; Man, Tianxing; Jonas, Steven J.; Schwartz, Jeffrey J.; Andrews, Anne M.; Weiss, Paul S. (July 2017, Nano Letters)
2. Green Lithography for Delicate Materials

   https://doi.org/10.1002/adfm.202101533  

   Grebenko, Artem; Bubis, Anton; Motovilov, Konstantin; Dremov, Viacheslav; Korostylev, Evgeny; Kindiak, Ivan; Fedorov, Fedor_S; Luchkin, Sergey; Zhuikova, Yuliya; Trofimenko, Aleksandr; et al (April 2021, Advanced Functional Materials)

   Abstract A variety of unconventional materials, including biological nanostructures, organic and hybrid semiconductors, as well as monolayer, and other low‐dimensional systems, are actively explored. They are usually incompatible with standard lithographic techniques that use harsh organic solvents and other detrimental processing. Here, a new class of green and gentle lithographic resists, compatible with delicate materials and capable of both top‐down and bottom‐up fabrication routines is developed. To demonstrate the excellence of this approach, devices with sub‐micron features are fabricated on organic semiconductor crystals and individual animal's brain microtubules. Such structures are created for the first time, thanks to the genuinely water‐based lithography, which opens an avenue for the thorough research of unconventional delicate materials at the nanoscale. 

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3. Robustify ML-Based Lithography Hotspot Detectors

   https://doi.org/10.1145/3508352.3549389  

   Pan, Jingyu; Chang, Chen-Chia; Xie, Zhiyao; Hu, Jiang; Chen, Yiran (October 2022, 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD))

   Deep learning has been widely applied in various VLSI design automation tasks, from layout quality estimation to design optimization. Though deep learning has shown state-of-the-art performance in several applications, recent studies reveal that deep neural networks exhibit intrinsic vulnerability to adversarial perturbations, which pose risks in the ML-aided VLSI design flow. One of the most effective strategies to improve robustness is regularization approaches, which adjust the optimization objective to make the deep neural network generalize better. In this paper, we examine several adversarial defense methods to improve the robustness of ML-based lithography hotspot detectors. We present an innovative design rule checking (DRC)-guided curvature regularization (CURE) approach, which is customized to robustify ML-based lithography hotspot detectors against white-box attacks. Our approach allows for improvements in both the robustness and the accuracy of the model. Experiments show that the model optimized by DRC-guided CURE achieves the highest robustness and accuracy compared with those trained using the baseline defense methods. Compared with the vanilla model, DRC-guided CURE decreases the average attack success rate by 53.9% and increases the average ROC-AUC by 12.1%. Compared with the best of the defense baselines, DRC-guided CURE reduces the average attack success rate by 18.6% and improves the average ROC-AUC by 4.3%. 

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4. DNA Nanostructures-Mediated Molecular Imprinting Lithography

   https://doi.org/10.1021/acsnano.6b04777  

   Tian, Cheng; Kim, Hyojeong; Sun, Wei; Kim, Yunah; Yin, Peng; Liu, Haitao (January 2017, ACS Nano)
5. Polymer-Pen Chemical Lift-Off Lithography

   https://doi.org/10.1021/acs.nanolett.7b01236  

   Xu, Xiaobin; Yang, Qing; Cheung, Kevin M.; Zhao, Chuanzhen; Wattanatorn, Natcha; Belling, Jason N.; Abendroth, John M.; Slaughter, Liane S.; Mirkin, Chad A.; Andrews, Anne M.; et al (May 2017, Nano Letters)