Block copolymer self-assembly : lithography, magnetic fabrication, and optimization

Abstract

Block copolymer (BCP) self-assembly is attractive because it provides nanoscale long-range ordered structures in a massive quantity. The capability of generating features with size as low as 5 nm is of particular interest in semiconductor fabrication since current photolithography has reached its resolution limitation and the other competing technologies are either too slow such as e-beam lithography or too expensive such as EUV system. In this thesis, BCP lithography is utilized to fabricate magnetic nanostructure and the corresponding magnetic properties are explored. The polystyrene- b-polydimethylsiloxane (PS-b-PDMS) diblock copolymer with different molecule weight is used to generate various sizes of robust silica pattern after solvent annealing and reactive ion etching. Pattern transfer methods are developed to convert the silica pattern into functional materials, including magnetic materials like cobalt, Co/Pd, FePt and CoFeB magnetic tunnel junctions (MTJ), and MoS2 monolayers. For magnetic nanowire arrays, the interactions between neighboring wires are investigated. For perpendicular MTJ nanopillar arrays, the size-dependent switching behavior and magnetostatic effects between two layers are analyzed. MoS 2 monolayers are patterned into features such as nanodots, nanorods and nanomeshs and the corresponding photoluminescence are characterized. Finally, machine learning and deep learning algorithms are the first-time ever demonstrated to model the BCP self-assembly process. The built model is able to recognize different BCP patterns and predicting the resulting morphology and pattern quality based on experimental process parameters. With this model, the BCP self-assembly can be further optimized toward industrial-grade production.