Caiwei Shang

Directed self‐assembly (DSA) provides a promising route for sub‐10‐nm patterning, but its integration into design‐technology co‐optimization (DTCO) remains limited by the difficulty of translating self‐assembly process constraints into manufacturable layout solutions. Here, an artificial‐intelligence‐driven closed‐loop simulation framework for DSA is presented to convert experimentally constrained DSA process knowledge into a process‐constrained design‐space map for DSA‐aware layout decomposition. The framework integrates a three‐dimensional Ohta‐Kawasaki model with Bayesian optimization and convolutional neural network screening, enabling automated model calibration using experimental racetrack‐template data without manual tuning. It efficiently identifies optimal model parameters, achieving a mean simulation‐experiment error below 1.5 nm while maintaining morphological consistency. In addition, four multimodal large language models are further evaluated for DSA morphology screening and are shown to adapt to new image modalities and defect criteria without additional task‐specific fine‐tuning, relying solely on text‐based rules and few‐shot examples. The calibrated model enables target‐constrained inverse design of experimentally validated template windows, translating DSA constraints into a manufacturable solution‐space map for layout decomposition. Its extension to peanut‐shaped templates without additional parameter calibration further supports DSA‐aware layout decomposition and DTCO. Overall, this work establishes an intelligent closed‐loop workflow linking experiment, simulation, and design, providing a practical route toward DTCO.

Directed self-assembly (DSA) of polystyrene-block-poly (methyl methacrylate) (PS-b-PMMA) has garnered substantial interest for semiconductor manufacturing, particularly for fabricating contact holes and vias. However, its application is limited by the low etch selectivity between the PS and PMMA domains. Here, we report an acid-responsive block copolymer, PS-N=CH-PMMA, incorporating a Schiff base (-N=CH-) linkage between the two blocks to impart acid sensitivity. The copolymer is synthesized via aldehyde-terminated PMMA (PMMA-CHO) precursors and is fully compatible with conventional thermal annealing workflows used for PS-b-PMMA. Uniform thin films with vertically oriented cylindrical domains were obtained, which could be directly converted into high-fidelity PS masks through acetic acid immersion without UV exposure. Graphoepitaxial DSA in 193i pre-patterned templates produced shrink-hole patterns with reduced critical dimension (CD) and improved local CD uniformity (LCDU). The shrink-hole CD was tunable by varying PMMA-CHO molecular weights. XPS confirmed selective cleavage of Schiff base linkages at the PS/PMMA interface under acidic conditions, while Ohta–Kawasaki simulations indicated interfacial wetting asymmetry governs etch fidelity and residual layer formation. Pattern transfer into TEOS layers was achieved with minimal CD loss. Overall, the acid-cleavable BCP enables scalable, high-fidelity nanopatterning with improved etch contrast, tunable process windows, and seamless integration into existing PS-b-PMMA lithography platforms.

Graphoepitaxial directed self-assembly (DSA) of block copolymers (BCPs) has emerged as a promising strategy for sub-30 nm line spacing patterning in semiconductor nanofabrication. Among the available BCP systems, polystyrene-block-poly (methyl methacrylate) (PS-b-PMMA) has been extensively utilized due to its well-characterized phase behavior and compatibility with standard lithographic processes. However, achieving a high-fidelity pattern with PS-b-PMMA remains challenging, owing to its limited etch contrast and reliance on UV-assisted degradation for PMMA removal. In this study, we report the synthesis of an acid-cleavable lamellar BCP, PS-N=CH-PMMA, incorporating a dynamic Schiff base (-N=CH-) linkage at the junction. This functional design enables UV-free wet etching, allowing selective removal of PMMA domains using glacial acetic acid. The synthesized copolymers retain the self-assembly characteristics of PS-b-PMMA and form vertically aligned lamellar nanostructures, with domain spacings tunable from 36.1 to 40.2 nm by varying the PMMA block length. When confined within 193i-defined trench templates with a critical dimension (CD) of 55 nm (trench width), these materials produced well-ordered one-space-per-trench patterns with interline spacings tunable from 15 to 25 nm, demonstrating significant line spacing shrinkage relative to the original template CD. SEM and FIB-TEM analyses confirmed that PS-N=CH-PMMA exhibits markedly improved vertical etch profiles and reduced PMMA residue compared to PS-b-PMMA, even without UV exposure. Furthermore, Ohta–Kawasaki simulations revealed that trench sidewall angle critically influences PS distribution and residual morphology. Collectively, this work demonstrates the potential of dynamic covalent chemistry to enhance the wet development fidelity of BCP lithography and offers a thermally compatible, UV-free strategy for sub-30 nm nanopatterning.