Kinetic Pathways of Block Copolymer Directed Self-Assembly: Insights from Efficient Continuum Modeling

Abstract

We introduce a computationally efficient continuum technique to simulate the complex kinetic pathways of block copolymer self-assembly. Subdiffusive chain dynamics is taken into account via nonlocal Onsager coefficients. An application to directed self-assembly of thin films of diblock copolymers on patterned substrates reveals the conditions under which experimentally observed metastable structures intervene in the desired thin-film morphology. The approach generalizes easily to multiblock copolymers and more complex guiding patterns on the substrate, and its efficiency allows for the systematic optimization of guiding patterns and process conditions.

Trendbericht: Makromolekulare Chemie

“…Dies schreiben etwa Rottler und Müller. 54) Neben einem besseren Verständnis für die Nanostrukturbildung ergeben sich zunehmend Anwendungen für diese komplexen Systeme. Zhang und Kolleg:innen haben gezeigt, wie eine Synergie aus Doppelbrechung und Oberflächen-Plasmonenresonanz wirkt.…”

Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches

“…11 This combination of self-assembly with lithography is termed directed self-assembly (DSA) and has been primarily directed towards applications in microelectronics, including memory storage materials, 6,12,13 finFET, 5,14,15 and vias. [16][17][18] These nanopatterned substrates have also seen use as catalysts for growth of ordered nanowire arrays, [19][20][21] as a platform for protein detection, 22,23 separation membranes, [24][25][26][27] surface enhanced Raman spectroscopy (SERS) substrates, [28][29][30] anti-reflective coatings in photovoltaics, [31][32][33] and chemical and biomedical sensors. [34][35][36][37] The self-assembly of a monolayer of a given BCP on a flat, featureless surface results in a polycrystalline morphology with uncorrelated nano-to micron-sized domains, with a significant concentration of defects.…”

“…Some kinetically trapped non-equilibrium metastable configurations in the free energy landscape persist even through long annealing times, which is an issue for high volume manufacturing on 300 mm wafers. 11,18,42,44 In an attempt to avoid defect formation during the course of self-assembly, much effort has been directed towards the optimization of annealing techniques of the spin-coated BCP films, including solvent vapor, [45][46][47] thermal (including microwave irradiation), [48][49][50][51] a combination of solvent and thermal (solvothermal), 52,53 laser annealing, 54,55 shear flow, 56,57 and annealing within an electric field. 58 Lithography multiplication via DSA also reduces defect density as chemical and morphological features on the surface help to guide the BCP into the lowest energy equilibrium patterns.…”

“…The spontaneous self-assembly of block copolymers (BCPs) to generate patterns and motifs with high resolution at low cost has been of great interest for over two decades as an alternative or complementary technique to photolithography. − When integrated with sparse guiding morphological or chemical features produced via traditional lithography, these low-cost polymer processing methods enable the generation of templates for the production of sub-10 nm features with a high degree of long-range order . This combination of self-assembly with lithography is termed directed self-assembly (DSA) and has been primarily directed toward applications in microelectronics, including memory storage materials, , finFET, , and vias. , These nanopatterned substrates have also seen use as catalysts for growth of ordered nanowire arrays , and as platforms for a diverse array of applications, including protein detection, , separation membranes, , surface-enhanced Raman spectroscopy (SERS) substrates, , anti-reflective coatings in photovoltaics, , and chemical and biomedical sensors. , …”

Dynamic coarse-graining of polymer systems using mobility functions

“…( 1) is Markovian and does not include memory effects. More general versions of (1) that includes a memory kernel K(r, r , t) have been proposed by Semenov [52] and more recently by Müller and coworkers [53,54]. Eq.…”