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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 (July 2021, Advanced Functional Materials)

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3. Tuning Young’s modulus in multiphoton lithography

   https://doi.org/10.1117/12.3005653  

   Mavrikos, Stefanos; Grigoropoulos, Costas P (March 2024, SPIE)

   Kabashin, Andrei V; Farsari, Maria; Mahjouri-Samani, Masoud (Ed.)

   Since Multiphoton Lithography (MPL) is applied as an additive manufacturing technique for the fabrication of operational microsystems, the need to predict the mechanical response of the fabricated structures emerges. This work focuses on determining the Young's Modulus of structures fabricated via MPL. With this objective in mind, two series of experiments were designed and conducted: the first one for the determination of the factors whose impact is significant, and the second one for generating a dataset used in the training of a machine learning tool that will define the suitable set of fabrication parameters for the fabrication of a structure with desired Young's Modulus. 

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4. 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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5. Bubble‐pen lithography: Fundamentals and applications

   https://doi.org/10.1002/agt2.189  

   Kollipara, Pavana Siddhartha; Mahendra, Ritvik; Li, Jingang; Zheng, Yuebing (March 2022, Aggregate)