Do-Nyun Kim

Constructing a dense focus–exposure matrix (FEM) for lithographic process-window analysis is often limited by the cost of acquiring (real or synthetic) scanning electron microscopy (SEM) images at every focus and dose condition. We propose a layout-to-SEM generative surrogate, PADE (process-adaptive denormalization), that injects the lithographic process condition (exposure dose, focus level, or both) into every denormalization layer of an image-to-image translation backbone, paired with a variational latent that supplies the cell-specific SEM texture. It can synthesize SEM images corresponding to unseen intermediate conditions in a single forward pass by interpolating the per-cell encoded latent and feeding the target condition directly to the generator after training on a sparse grid of focus–dose anchors only. On a 3×3 focus–dose anchor grid over 40 line–space cells rendered by a physics-based simulation pipeline, the generated SEM images successfully reconstruct the simulated FEM with accurate metrology metrics. For example, the critical dimension measured from the generated SEM images precisely tracks the simulated ground-truth values with a Pearson correlation of r=0.999 and a root-mean-square error of 1.72 nm. The exposure latitude extracted from the generated Bossung surfaces also agrees well with the ground truth at r=0.97. Once trained, PADE can produce SEM images almost in real time (7108 times faster than conventional physics-based simulation), enabling continuous Bossung-curve extraction and comprehensive process-window analysis in process development.

Generative models that have advanced inverse design in protein engineering can be extended to DNA origami to explore broader design spaces enabling complex geometries and functions for emerging nanotechnologies. However, progress in generative DNA origami design has been limited by the lack of large, standardized structural datasets containing structural information. To address this challenge, we introduce a diffusion-based generative design framework trained on simulated equilibrium conformations obtained using a multiscale computational model. Given user-defined target geometries, our model produces physically plausible DNA origami designs through guided diffusion sampling and strand routing, with integrated structure prediction for quantitative evaluation. Among more than 100 generated candidates, selected structures are experimentally validated, demonstrating proper folding and functional behaviors such as auxetic transformation and modular assembly. Our results highlight the potential of generative modeling for complex DNA origami design, expanding the accessible design space and facilitating the creation of sophisticated, reconfigurable architectures.

DNA origami nanostructures can adopt multiple conformational states, making an accurate and rapid classification essential for advancing nanoscale fabrication. Although atomic force microscopy (AFM) provides indispensable structural validation, manual per-object labeling for classification is a persistent bottleneck. Here, we introduce an automated classification framework that minimizes user annotation while maintaining high accuracy. The pipeline employs a confidence-based filter to flag uncertain or aggregated objects, which are then selectively relabeled and augmented for iterative retraining. This human-in-the-loop refinement enables the classifier to capture greater intraclass morphological diversity without extensive new labeling. With only 20 labeled images per class expanded through strong augmentation, our framework consistently outperforms established few-shot and contrastive learning baselines on two structurally distinct DNA origami data sets, while also shortening analysis time relative to conventional methods, enabling efficient, high-throughput, and objective classification of multistate DNA nanostructure populations.

MRNA-DNA hybrid origami enables integration of the RNA functionality into programmable DNA nanostructures, yet robust design and assembly rules remain lacking. Here, we systematically define parameters governing the high-yield formation of compact mRNA-DNA hybrid origami. Using mature mRNAs encoding firefly luciferase, enhanced green fluorescent protein (EGFP), and mCherry as scaffolds, we designed five architectures spanning varied sizes, shapes, crossover geometries, and packing densities. We identify asymmetric A-form crossovers, monovalent-cation-rich buffers, and moderate-temperature annealing as critical for suppressing RNA degradation and kinetic trapping while accommodating RNA-DNA helical geometry. Atomic force microscopy confirms monodisperse, well-folded nanostructures with nanoscale precision comparable to that of DNA origami. These results establish generalizable design rules and a standardized synthesis protocol for mRNA-DNA hybrid origami.

Structural DNA nanotechnology enables the programmable construction of nano- to microscale assemblies with high resolution, yet predicting their mechanical behavior prior to synthesis remains challenging. In this paper, we introduce SNUPI, a computational framework that predicts the shape and mechanical responses of structured DNA assemblies using finite element-based analysis. SNUPI uses design files from caDNAno and performs static analysis, normal-mode analysis, and Langevin dynamics simulations to evaluate equilibrium configurations, dominant deformation modes, thermal fluctuations, and intercalator-induced effects. We demonstrate that SNUPI can offer an efficient and accessible platform for pre- and postsynthetic evaluation, facilitating rational design and analysis of complex DNA architectures.

MRNA–DNA hybrid origami offers a powerful route to combine the structural programmability of DNA origami with the biological functionality of messenger RNA, but generalizable design and assembly rules for these hybrids remain poorly defined. Here we systematically investigate the design principles and synthesis conditions that govern high-yield formation of mRNA–DNA hybrid nanostructures. Using mature mRNAs encoding firefly luciferase, EGFP, and mCherry as scaffolds, we construct a series of five hybrid compact origamis with diverse sizes, shapes, crossover strategies, and packing densities. We identify key parameters that control folding fidelity, including asymmetric A-form crossovers, monovalent-cation concentrations, and moderate-temperature annealing protocols, which together mitigate RNA instability, reduce kinetic traps, and accommodate RNA–DNA helical geometry. Atomic force microscopy reveals monodisperse, well-folded structures consistent with design expectations across most architectures and confirms that optimized conditions produce nanoscale precision comparable to DNA origami. Our findings establish generalizable design rules and a standard synthesis protocol for mRNA–DNA hybrid origami, providing a framework for their use in gene delivery and other RNA-based nanotechnologies.

This study demonstrates that scaffolded DNA origami can be successfully assembled using significantly elongated staple strands, thereby enabling the effective integration of enzymatic DNA production methods into DNA nanotechnology.