Litcius/Paper detail

Synthesis and Characterization of Block Copolymers for Nanolithography Based on Thiol‐Ene “Click” Functionalized Polystyrene‐Block‐Polybutadiene

Hongbo Feng, Moshe Dolejsi, Ning Zhu, Philip J. Griffin, Gordon S. W. Craig, Wen Chen, Stuart J. Rowan, Paul F. Nealey

2022Advanced Functional Materials15 citationsDOIOpen Access PDF

Abstract

Abstract In developing block copolymers (BCPs) for nanolithography, the preferred materials and processes are BCPs that can be thermally annealed with a free surface to yield perpendicularly orientated nanodomains with specific feature sizes and morphologies. Thiol‐ene “click” chemistry is used to modify a single parent polystyrene‐block‐poly(1,2‐butadiene) (PS‐ b ‐PB) to create both cylinder‐ and lamellae‐forming A‐block‐(B‐random‐C) BCPs with different Flory–Huggins parameters (χs) while maintaining an equal surface energy (γ, Δγ = 0) between the blocks, necessary to form perpendicular nanodomains by thermal annealing with a free surface. The use of BCPs with an A‐block‐(B‐random‐C) architecture effectively decouples the covarying properties of χ and Δγ. The effects of the size of the thiol and degree of thiol functionalization (φ) on χ, morphology, and Δγ of the blocks are investigated for four different thiols. Modification of PS‐ b ‐PB with methyl thioglycolate or 2‐(Boc‐amino)ethanethiol creates BCPs that form cylinders, whereas modification with smaller thiols, mercaptoethanol or 1‐thioglycerol, retains the lamellar morphology of the parent PS‐ b ‐PB. Cross‐linking of the double bonds in PB at annealing temperatures prevents directed self‐assembly (DSA) of these BCPs, but by adding a radical scavenger, butylated hydroxytoluene, the cross‐linking can be suppressed, enabling DSA.

Topics & Concepts

Materials scienceCopolymerPolystyreneSurface modificationPolybutadieneGyroidAnnealing (glass)Click chemistryPolymer chemistryChemical engineeringComposite materialPolymerEngineeringBlock Copolymer Self-AssemblyAdvanced Polymer Synthesis and CharacterizationMachine Learning in Materials Science