Abstract
Self-assembly of block copolymers (BCPs) is an alternative patterning technique that promises high resolution and density multiplication with lower costs. The defectivity of the resulting nanopatterns remains too high for many applications in microelectronics and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapor pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flow-controlled system is combined with design of experiments (DOE) and machine learning (ML) approaches. The SVA flow-controlled system enables precise optimization of the conditions of self-assembly of the high Flory-Huggins interaction parameter (χ) hexagonal dot-array forming BCP, poly(styrene-b-dimethylsiloxane) (PS-b-PDMS). The defects within the resulting patterns at various length scales are then characterized and quantified. The results show that the defectivity of the resulting nanopatterned surfaces is highly dependent upon very small variations of the initial film thicknesses of the BCP, as well as the degree of swelling under the SVA conditions. These parameters also significantly contribute to the quality of the resulting pattern with respect to grain coarsening, as well as the formation of different macroscale phases (single and double layers and wetting layers). The results of qualitative and quantitative defect analyses are then compiled into a single figure of merit (FOM) and are mapped across the experimental parameter space using ML approaches, which enable the identification of the narrow region of optimum conditions for SVA for a given BCP. The result of these analyses is a faster and less resource intensive route toward the production of low-defectivity BCP dot arrays via rational determination of the ideal combination of processing factors. The DOE and machine learning-enabled approach is generalizable to the scale-up of self-assembly-based nanopatterning for applications in electronic microfabrication.
Highlights
The spontaneous self-assembly of block copolymers (BCPs) to generate patterns and motifs with sub-lithographic resolution has been of great interest for over two decades as an alternative or complementary technique to photolithography.[1,2,3,4,5,6,7,8,9,10] 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 production of sub-10 nm features with a high degree of long range order.[11]
The results show that defectivity of the resulting nanopatterned surfaces are highly dependent upon very small variations of the initial film thicknesses of the BCP, as well as the degree of swelling under the solvent vapor annealing (SVA) conditions
The results of qualitative and quantitative defect analyses are compiled into a single figure of merit (FOM) using Design of Experiments (DOE) and machine learning (ML) approaches, which enable identification of the narrow region of optimum conditions for SVA for a given BCP
Summary
The spontaneous self-assembly of block copolymers (BCPs) to generate patterns and motifs with sub-lithographic resolution has been of great interest for over two decades as an alternative or complementary technique to photolithography.[1,2,3,4,5,6,7,8,9,10] 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 production of sub-10 nm features with a high degree of long range order.[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 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]. Lithography multiplication via DSA reduces defect density as chemical and morphological features on the surface help to guide the BCP into the lowest energy equilibrium patterns.[8,10,42,59,60] Defects still remain a challenge, with smaller molecular weight BCPs with a high Flory-Huggins interaction parameter (χ! > 10.5) that produce the sub-20 nm periodicities,[10,61,62] and features.[9]
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