Ginige Gayashani, Song Youngdong, Olsen Brian C, Luber Erik J, Yavuz Cafer T, Buriak Jillian M
Department of Chemistry, University of Alberta, 11227-Saskatchewan Drive, Edmonton, Alberta T6G 2G2, Canada.
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
ACS Appl Mater Interfaces. 2021 Jun 23;13(24):28639-28649. doi: 10.1021/acsami.1c05056. Epub 2021 Jun 8.
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--dimethylsiloxane) (PS--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.
嵌段共聚物（BCP）的自组装是一种替代性的图案化技术，有望以较低成本实现高分辨率和密度倍增。对于许多微电子应用而言，所得纳米图案的缺陷率仍然过高，并且诸如膜厚等加工参数的微小变化以及溶剂蒸气压和温度的波动等因素会使其进一步恶化。在这项工作中，将溶剂蒸汽退火（SVA）流量控制系统与实验设计（DOE）和机器学习（ML）方法相结合。SVA流量控制系统能够精确优化具有高弗洛里-哈金斯相互作用参数（χ）的形成六边形点阵列的BCP（聚（苯乙烯-二甲基硅氧烷）（PS-PDMS））的自组装条件。然后对所得图案在各种长度尺度下的缺陷进行表征和量化。结果表明，所得纳米图案化表面的缺陷率高度依赖于BCP初始膜厚的非常小的变化以及SVA条件下的溶胀程度。这些参数对于所得图案在晶粒粗化以及不同宏观相（单层和双层以及润湿层）形成方面的质量也有显著贡献。然后将定性和定量缺陷分析的结果汇总为一个单一的品质因数（FOM），并使用ML方法在实验参数空间中进行映射，这能够识别给定BCP的SVA最佳条件的狭窄区域。这些分析的结果是通过合理确定加工因素的理想组合，实现生产低缺陷率BCP点阵列的更快且资源消耗更少的途径。DOE和机器学习支持的方法可推广到基于自组装的纳米图案化在电子微加工中的放大应用。
